CN116883196A - Building source load storage cooperative operation method, system and equipment for energy storage by ground source heat pump - Google Patents
Building source load storage cooperative operation method, system and equipment for energy storage by ground source heat pump Download PDFInfo
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Abstract
The invention provides a building source load storage cooperative operation method, a system and equipment for energy storage by a ground source heat pump, which comprise the following steps: acquiring day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of a building energy center in combination with a pre-constructed day-ahead optimization model; the method comprises the steps of obtaining a daily scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source based on a daily scheduling plan of different micro source start-stop, time-shifting load and energy storage and a real-time electricity price in combination with a pre-built daily optimization model; and the operation of the building source load storage is cooperatively optimized based on the daily scheduling plan of controllable unit output, adjustable load and energy storage power in the micro source. The method establishes the source charge storage day-ahead optimization model and introduces the real-time electricity price day-ahead optimization model, and establishes the strategy based on two time scales, so that the problem that the conventional system is difficult to make a decision on the optimal scheme in uncertainty and rapid change of electricity price is solved, and the multi-energy comprehensive utilization and overall economic operation are realized.
Description
Technical Field
The invention relates to the technical field of energy conservation and resource comprehensive utilization, in particular to a method, a system and equipment for collaborative operation of building source load and storage by using a ground source heat pump for energy storage.
Background
BEMS is an intelligent building energy regulation and control decision center, and is used for monitoring, automatically controlling and optimally managing building energy equipment to realize the management of the building energy equipment and energy. In order to reduce the overall energy consumption of the building, besides optimizing single equipment and links, an optimal overall energy efficiency management scheme needs to be comprehensively considered at a system level. Therefore, the energy flow of the building energy system is analyzed, the energy flow is regulated and controlled through the information flow to control equipment and manage energy, and the safe, reliable and economic operation of the energy system is ensured on the premise of meeting the requirements of ASHRAE standards on indoor air quality and temperature and humidity.
The distributed photovoltaic and ground source heat pump utilizes renewable solar energy to realize low-carbon operation of the building, and energy storage can solve the problem of random fluctuation of photovoltaic power generation output, and greatly improve the flexibility of building energy, so that the excavation space of building economic energy utilization behavior is expanded. In order to accomplish the optimal management of modern building electric energy, heat energy, fuel gas, fire coal and other energy forms, BEMS needs to face more uncertainty and complex coupling between various energies, and fast changes of market excitation signals such as actual electricity prices and the like, and in these cases, an optimal energy distribution scheme is rapidly determined, while the automation degree of a traditional building energy management system is lower, and the algorithm is simple, and in face more uncertainty and complex coupling between various energies, and fast changes of market excitation signals such as actual electricity prices and the like, an optimal energy distribution scheme cannot be rapidly determined.
Disclosure of Invention
In order to solve the problem that the traditional building energy management system cannot quickly determine the optimal energy distribution scheme under the conditions of more uncertainty and complex coupling among various energies and rapid change of market excitation signals such as real electricity price, the invention provides a building source load storage cooperative operation method for energy storage by a ground source heat pump, which comprises the following steps:
acquiring day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of a building energy center in combination with a pre-constructed day-ahead optimization model;
based on the different micro-source start-stop, time-shifting load and energy storage day-ahead scheduling plans and real-time electricity price, and a pre-built day-ahead optimization model, obtaining a controllable unit output, an adjustable load and energy storage day-ahead scheduling plan in the micro-source;
and the operation of the building source load storage is cooperatively optimized based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
Preferably, the construction of the day-ahead optimization model includes:
determining conversion relations of two sides of energy supply and demand based on micro-source, load and energy storage data of the building energy center and an energy system coupling matrix;
The minimum energy cost meeting the conversion relation is used as a first objective function;
taking the time-shift load translation starting moment constraint and the time-shift load quantity constraint as a first constraint condition;
and constructing a day-ahead optimization model by the first objective function and the first constraint condition.
Preferably, the construction of the daily optimization model includes:
-determining a second objective function from said economy and comfort satisfying a conversion relation;
with indoor illuminance constraint, indoor temperature constraint and indoor CO 2 Concentration constraint, adjustable load starting time constraint and adjustable load adjustment quantity constraint are second constraint conditions;
and constructing an intra-day optimization model by the second objective function and the second constraint condition.
Preferably, the first objective function is represented by the following formula:
wherein, C is the energy cost in the optimization period before the day; c (C) f (t) is the t period outsourcing fuel cost; c (C) dg (t) is the t period grid interaction cost; c (C) om (t) is a t-period operation maintenance cost; c (C) cs (t) the start-stop cost of the time period controllable unit; c (C) RL And (t) adjusting the cost for the t period load.
Preferably, the second objective function is represented by the following formula:
min F＝wC 1 +y(1-w)(1-A)；
wherein w is the weight between the economy and the comfort level set by the user according to the actual demand; y is the difference between the two dimensions used for balancing; c (C) 1 Optimizing the energy consumption cost in the period for the day; a is comfort level; f is the comprehensive optimization target value.
Preferably, the micro-source, load and energy storage data of the building energy center include: ground source heat pump electric power, gas boiler pressure, gas boiler temperature, distributed photovoltaic efficiency, photovoltaic module parameters, heat storage tank climbing rate, storage battery maximum daily replay times, gas storage tank gas storage capacity, indoor temperature and humidity, time-shifting load capacity, adjustable load capacity and electric equipment total power.
Preferably, the conversion relation is calculated as follows:
L(t)＝C(t)P(t)；
wherein L (t) is the load side; p (t) is the energy supply side; c (t) is a coupling matrix of an energy system inside the energy center.
Preferably, the method for obtaining the day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of the building energy center combined with a pre-built day-ahead optimization model comprises the following steps:
and optimizing building integrated photovoltaic power generation power, load time-by-time power, counted time-of-use electricity price and gas price at a first time interval based on micro-source, load and energy storage data of the building energy center to obtain a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage of the next day.
Preferably, the obtaining the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source based on the daily scheduling plan and the real-time electricity price of the different micro source start-stop, the time-shifting load and the energy storage combined with a pre-built daily optimization model includes:
reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters;
and optimizing the initialization parameters by rolling at a second time interval, and introducing real-time electricity price as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day.
Preferably, the method further comprises:
obtaining unbalanced electric power based on the difference value between the real-time load of the building energy center and the daily optimized load;
and correcting the unbalanced electric power in real time based on the optimized electric load in the day, and determining a corrected adjustable load plan.
The invention also provides a building source load storage cooperative operation system for energy storage by the ground source heat pump based on the same invention conception, which comprises the following steps:
the day-ahead plan generation module is used for obtaining day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of the building energy center in combination with a pre-built day-ahead optimization model;
The intra-day plan generation module is used for obtaining an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in the micro source based on the pre-day scheduling plans of the different micro source start-stop, time-shifting load and energy storage and a real-time electricity price in combination with a pre-built intra-day optimization model;
and the collaborative optimization module is used for collaborative optimization of the operation of the building source load storage based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
Preferably, the daily schedule generating module is specifically configured to:
and optimizing building integrated photovoltaic power generation power, load time-by-time power, counted time-of-use electricity price and gas price at a first time interval based on micro-source, load and energy storage data of the building energy center to obtain a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage of the next day.
Preferably, the intra-day plan generation module is specifically configured to:
reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters;
and optimizing the initialization parameters by rolling at a second time interval, and introducing real-time electricity price as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day.
In yet another aspect, the present application also provides a computer device, including:
one or more processors;
the processor is used for storing one or more programs;
when the one or more programs are executed by the one or more processors, the building source load storage collaborative operation method for the energy storage of the ground source heat pump is realized.
In still another aspect, the present application further provides a computer readable storage medium, which is characterized in that a computer program is stored thereon, and when the computer program is executed, the method for collaborative operation of building source load storage by using a ground source heat pump for energy storage is implemented.
Compared with the prior art, the application has the beneficial effects that:
the application provides a building source load storage cooperative operation method for energy storage by a ground source heat pump, which comprises the following steps: acquiring day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of a building energy center in combination with a pre-constructed day-ahead optimization model; based on the different micro-source start-stop, time-shifting load and energy storage day-ahead scheduling plans and real-time electricity price, and a pre-built day-ahead optimization model, obtaining a controllable unit output, an adjustable load and energy storage day-ahead scheduling plan in the micro-source; and the operation of the building source load storage is cooperatively optimized based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source. According to the application, a day-ahead optimization model of different micro sources, loads and energy storage and a day-ahead optimization model of a real-time electricity price are built, a source and storage collaborative optimization strategy is formulated based on two time scales in the day ahead and the day, and under the conditions of more uncertainty and complex coupling among various energies and rapid change of market excitation signals such as real-time electricity price, an optimal energy distribution scheme can be rapidly determined, and the comprehensive utilization of multiple energies and the accurate distribution of energy sources are realized.
Drawings
FIG. 1 is a flow chart of a construction source load storage cooperative operation method for energy storage by a ground source heat pump;
FIG. 2 is a basic block diagram of a building energy center of the present invention;
FIG. 3 is a schematic diagram of a multi-time scale optimization strategy for source load storage in a building energy center according to the present invention;
FIG. 4 is a flow chart of collaborative optimization scheduling of day-ahead and day-ahead lotus stores of the building energy center according to the invention;
fig. 5 is a flow chart of a building source load storage cooperative operation system for energy storage by a ground source heat pump.
Detailed Description
For a better understanding of the present invention, reference is made to the following description, drawings and examples.
Example 1:
the invention provides a building source load storage cooperative operation method for energy storage by a source heat pump, which is shown in figure 1 and comprises the following steps:
step 1: acquiring day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of a building energy center in combination with a pre-constructed day-ahead optimization model;
step 2: based on the different micro-source start-stop, time-shifting load and energy storage day-ahead scheduling plans and real-time electricity price, and a pre-built day-ahead optimization model, obtaining a controllable unit output, an adjustable load and energy storage day-ahead scheduling plan in the micro-source;
Step 3: and the operation of the building source load storage is cooperatively optimized based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
The day-ahead optimization model mentioned in this embodiment is constructed based on micro-source, load and energy storage data of the building energy center, with minimum energy cost as an objective function, with time-shift load translation start time constraint and time-shift load quantity constraint as constraint conditions, and the construction of the day-ahead optimization model is described first:
the construction process of the day-ahead optimization model is as follows:
a1, determining conversion relations of two sides of energy supply and demand based on micro-source, load and energy storage data of the building energy center and an energy system coupling matrix;
step A2, the minimum energy cost meeting the conversion relation is used as a first objective function;
step A3, restraining the time-shift load translation starting moment and the time-shift load quantity as a first constraint condition;
and A4, constructing a day-ahead optimization model by the first objective function and the first constraint condition.
In step A1, determining a conversion relationship between the supply and demand of energy sources based on micro-source, load and energy storage data of the building energy center in combination with an energy system coupling matrix, including:
Firstly, the micro source, load and energy storage data of the building energy center mentioned in the embodiment are described in detail:
the basic structure of the energy concentrator of the building energy center is shown in fig. 2, wherein the energy supply unit comprises distributed photovoltaics, a ground source heat pump and a gas boiler, the energy storage unit comprises heat storage, electricity storage and gas storage, and the load is divided into electric load, thermal load and cold load.
(1) Basic data of equipment such as a ground source heat pump, a gas boiler, a distributed photovoltaic and the like in an energy supply unit are acquired, and the basic data include but are not limited to:
ground source heat pumps, such as refrigeration capacity and heating capacity, refrigeration and heating efficiency, electric power, temperature, flow, pressure, ramp rate, and the like;
gas boilers such as heating capacity, heat efficiency, evaporation capacity, pressure, temperature, climbing rate, etc.;
distributed photovoltaics, such as capacity, efficiency, photovoltaic module parameters, and the like.
(2) Basic data of equipment such as a heat storage water tank, a storage battery and the like in the energy storage unit is acquired, including but not limited to:
a thermal storage tank such as an amount of stored heat or cold, efficiency, pressure, temperature, ramp rate, etc.;
storage batteries such as capacity, efficiency, operating temperature, ramp rate, maximum daily charge and discharge times, etc.;
Air reservoirs, such as air reservoir, pressure, flow, etc.
(3) Obtaining side-of-charge data, including but not limited to:
heat load or cold load such as indoor temperature and humidity, effective area, house thermal resistance, etc.;
the electrical load, e.g., the time-shifted load amount, the adjustable load amount, the total power of the powered device, etc.
The following description is made on the conversion relationship between the supply and demand of energy source in this embodiment:
establishing a conversion matrix for connecting the two sides of energy supply and demand, comprising:
according to the conversion and distribution process of various energy sources such as electricity, heat and the like which are shown in the energy concentrator, the conversion relationship of the two sides of the energy source supply and demand is obtained as follows:
L(t)＝C(t)P(t)；
wherein: l (t) represents the load side, specifically L (t) = [ L e (t) L h (t) L c (t)] T The method comprises the steps of carrying out a first treatment on the surface of the P (t) represents an energy supply side, specifically, P (t) = [ P ] dg P pv P se P bo P hp P sc P sh ] T The method comprises the steps of carrying out a first treatment on the surface of the C (t) represents a coupling matrix of an energy system in the energy center, and the coefficient size of the coupling matrix is related to energy conversion efficiency, scheduling coefficient and constraint conditions.
Wherein: c (t) represents a coupling matrix of an energy system in an energy center, eta tr Indicating the transformation efficiency of the distribution transformer, u 1 (t) is the load factor of the t period; η (eta) pv Represents the inversion efficiency of photovoltaic power generation, u 2 (t) is the force coefficient of the t period; η (eta) se For the conversion efficiency of the accumulator, u 3 (t) is the battery charge or discharge coefficient; η (eta) bo Represents the heating efficiency of the gas boiler, m 1 (t) represents the heating load coefficient of the gas boiler in the period t, and 0 is taken when the gas boiler is not heated; η (eta) hph Represents the heating efficiency of the heat pump unit, m 2 (t) represents a heating load factor of the heat pump; η (eta) sh For the conversion efficiency of the heat storage water tank, m 3 (t) is a storage or release coefficient; η (eta) hpc Represents the refrigerating efficiency of the heat pump unit, n 1 (t) represents a refrigeration load factor of the heat pump; η (eta) a Indicating the efficiency of the common air conditioning system, n 2 (t) represents a common air conditioner refrigeration load factor, and 0 is taken when no other air conditioner is refrigerated; n is n 3 (t) is a storage or release coefficient; η (eta) sc The conversion efficiency of the heat storage water tank.
The plurality of coefficients mentioned in C (t) are related to the respective device operating conditions and load rates, each coefficient being constant while the device operating conditions and load rates remain unchanged. Each element in P (t) is the rated power or installed capacity of the device under a specific operating condition.
Determining constraints of the unified mathematical model, comprising:
setting unit operation constraints of a heat pump unit, photovoltaic power generation, a gas boiler, a heat storage water tank, a storage battery and the like;
and setting the power interaction constraint of the public distribution network and the building energy center.
In step A2, the minimum energy cost satisfying the conversion relation is a first objective function, which specifically includes:
Wherein, C is the energy cost in the optimization period before the day; c (C) f (t) is the t period outsourcing fuel cost; c (C) dg (t) is the t period grid interaction cost; c (C) om (t) is a t-period operation maintenance cost; c (C) cs (t) the start-stop cost of the controllable unit in the t period; c (C) RL And (t) adjusting the cost for the t period load.
The first objective function of the day-ahead optimization model may be represented by decision variables as:
min C＝f(U bo ，U pv ，U hp ，U dg ，P TL )；
wherein: u (U) bo 、U pv 、U hp And U dg Respectively represents the start-stop state and the stop state of a gas boiler, a photovoltaic ground source heat pump and a power distribution network, P TL Representing the amount of time-shiftable load.
In the step A3, the time-shift load translation starting time constraint and the time-shift load quantity constraint are used as a first constraint condition, which specifically includes:
the first load constraint condition of the day-ahead optimization model is:
time-shift load translation start time constraint:
wherein t is 1 Is the starting time of the time-shift load;is the set lower limit time; />The upper limit time is set; Δt (delta t) TL Representing the time-lapse load duration. In the formula, each variable unit is hour.
Time-shiftable load amount constraint:
in the method, in the process of the invention,the number of maximum load units time-shifted for the period t; m is M TL (t) is the number of load units shifted in the optimization period.
The time-shifting load mentioned in the embodiment refers to the load of which the power consumption time period is controlled by the system comprehensive demand response scheduling center under the condition that the power consumption time of the load is unchanged. The mapping relationship between the time-shifted load and the time-shifted load power which is not regulated by the integrated demand response, and the load power which is accessed and shifted out after the integrated demand response regulation can be expressed as follows:
Wherein: p (P) TL (t) time-shifted load adjusted by the integrated demand response for time period t;time-shifted load power for the ith user of period t, not adjusted by the integrated demand response; />The load power of the i-th user in the t period is accessed and removed after the comprehensive demand response adjustment; n (N) TL To the number of users participating in the time-lapse load response; t (T) in 、T out The time shift load switching-in period and the time shift load switching-out period are respectively carried out; />Load power shifted in for the t period; />Load power shifted out for period t.
And A4, constructing a day-ahead optimization model by the first objective function and the first constraint condition.
In this embodiment, the obtaining a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage based on the micro-source, load and energy storage data of the building energy center in step 1 in combination with a pre-built day-ahead optimization model includes:
and optimizing building integrated photovoltaic power generation power, load time-by-time power, counted time-of-use electricity price and gas price at a first time interval based on micro-source, load and energy storage data of the building energy center to obtain a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage of the next day.
Further, based on micro-source, load and energy storage data of the building energy center, optimizing building integrated photovoltaic power generation power, load time-by-time power, time-of-use electricity price and gas price at a first time interval to obtain day-ahead scheduling plans of different micro-source start-stop, time-of-use load and energy storage of the next day, specifically comprising:
As shown in fig. 3, the day-ahead optimization predicts the building integrated photovoltaic power generation power and load time-by-time power of the next day, takes market excitation signals such as time-of-use electricity price, gas price and the like into account, optimizes the start-stop, time-shift load and energy storage state of the controllable power supply at intervals of 1 hour, and as shown in fig. 4, the day-ahead optimization algorithm makes a start-stop plan of a gas boiler, a ground source heat pump, a power distribution network and a photovoltaic of the next day and a scheduling plan of the time-shift load and energy storage according to initial values of various parameters.
The intra-day optimization model mentioned in this embodiment is based on a day-ahead schedule, and uses economy and comfort satisfying a conversion relationship as an objective function, and uses indoor illuminance constraint, indoor temperature constraint, and indoor CO 2 Concentration constraint, adjustable load starting time constraint and adjustable load adjustment quantity constraint are constructed by constraint conditions, and construction of a daily optimization model is described herein:
the construction process of the daily optimization model is as follows:
step B1, the economy and comfort degree meeting the conversion relation are used as a second objective function;
step B2 is to restrict indoor illuminance, indoor temperature and indoor CO 2 Concentration constraint, adjustable load starting time constraint and adjustable load adjustment quantity constraint are second constraint conditions;
And B3, constructing an intra-day optimization model according to the second objective function and the second constraint condition.
In step B1, the economical efficiency and comfort level satisfying the conversion relation are second objective functions, which specifically include:
the second objective function of the intra-day phase optimization model is:
economic objectives:
wherein C is 1 Optimizing the energy consumption cost in the period for the day;the cost of outsourcing fuel for the t period; />The interaction cost of the power grid in the t period; />Operating maintenance cost for the period t; />The cost is adjusted for t-period load. In the formula, the unit of each variable is ten thousand yuan.
The second objective function of the intra-day optimization model may be represented by decision variables as:
min F＝g(P bo ，P pv ，P hp ，P dg ，P se ，P sh ，P sc )；
wherein: f represents a comprehensive optimization target value, P bo 、P pv 、P hp 、P dg 、P se 、P sh And P sc Respectively representing heat accumulation or cold accumulation power of a gas boiler, a photovoltaic, a ground source heat pump, a power distribution network, a storage battery and a heat accumulation water tank.
Comfort level target:
the lighting comfort index is characterized by an indoor illuminance that varies within a user acceptable range.
Wherein A is 1 (n, t) is the lighting comfort of the nth class room for period t; e (E) s The unit is 1x for indoor standard illumination; e (n-t) represents the illuminance of the nth type room in the t period.
Thermal comfort is characterized by an indoor temperature that varies within a range of allowable values set by a user.
Wherein A is 2 (n, t) is the temperature comfort of the nth class room for period t; t (T) s The unit is the indoor standard temperature; t (T) room (n, t) is the room temperature of the nth class room at the end of the t period.
CO for air quality comfort 2 Concentration characterization, indoor CO 2 The concentration varies within a range of allowable values set by the user.
Wherein A is 3 (n, t) air quality comfort for a class n room for a period t; n (N) s Is indoor standard CO 2 Concentration in ppm; n (N, t) is the indoor CO of the nth room at the end of the t period 2 Concentration values.
A(n，t)＝aA 1 (n，t)+bA 2 (n，t)+cA 3 (n，t)；
Where a (n, t) is the comfort level of the nth class room for the period t. Wherein a, b, c are set according to the preference of the user and satisfy a+b+c=1, herein taken a=b=c; a is that 1 (n, t) Lighting comfort for a class n room for period t, A 2 (n, t) temperature comfort of the nth class room for t period, A 3 (n, t) is the air quality comfort level of the nth class room for the t period.
Comprehensive optimization targets:
the daily optimization considers the dual targets of economy and comfort, the multi-target optimization is converted into single-target optimization solution by adopting a weighted aggregation method, and the comprehensive optimization targets are as follows:
min F＝wC 1 +y(1-w)(1-A)；
wherein: w is the weight between the economy and the comfort level set by the user according to the actual demand; y is the difference between the two dimensions used for balancing; a is comfort level; f is the comprehensive optimization target value.
In the step B2, the indoor illumination constraint, the indoor temperature constraint and the indoor CO 2 Concentration constraints, adjustable load start time constraints, adjustableThe load adjustment amount constraint is a second constraint condition, specifically including:
the user comfort quantization index is designed in this embodiment as follows:
the illumination comfort level, the thermal comfort level and the indoor air quality comfort level are three main factors influencing the user comfort level, and the corresponding quantization indexes are illumination, temperature and humidity and CO 2 Concentration.
The power of the controllable lighting equipment can be continuously adjusted within a certain range, namely the indoor illuminance changes along with the increase and decrease of the number of the lighting equipment, and the illuminance mathematical model can be expressed as
Wherein: e (n, t) represents the indoor illuminance of the nth type room in lx in t time period; b (n) and m (k) respectively represent the number of indoor light sources in the nth room and the luminous flux of each k light sources, and the units are respectively the class and lm; x, Y is the utilization coefficient and maintenance coefficient of the light source, and represents the effective utilization degree and the light loss degree of the luminous flux; s (n) represents the illuminated area of the nth type room in m 2 。
For air conditioning equipment, establishing a mathematical model of the relation between air conditioning energy consumption and indoor and outdoor temperatures as
Wherein: t (T) room (n,t)、T out (n, t) respectively representing the indoor temperature and the outdoor temperature of the nth room at the end of the t period, wherein the units are DEG C; t (T) room (n, t-1) represents the indoor temperature of the nth class room at the end of the t-1 period; r is R eq The equivalent thermal resistance of the room is shown as omega; m is M air Representing indoor air quality; c (C) P The specific heat of air is expressed as J/(kg.K); q (n, t) represents the heat transferred from the room by the room air conditioning system of the nth type for the period of t, in J.
Indoor CO for indoor air quality 2 Concentration is characterized, requiring at the userThe set allowable value range is changed. To maintain indoor CO 2 The concentration is stable, a certain fresh air quantity is required to be provided for the room, and the indoor CO 2 The mathematical relationship between the concentration and the fresh air quantity is expressed as
Wherein: n (N, t) represents the indoor CO of the nth room at the end of the t period 2 Concentration values in ppm; n (N, t-1) represents the indoor CO of the nth class room at the end of the t-1 period 2 Concentration value, B represents fresh air flow, and the unit is m 3 /s；N W Representing CO in outdoor air 2 The concentration of the water in the water is higher,representing indoor CO of an nth class room 2 Is a rate of production of (1); v represents the total volume of the room, unit m 3 The method comprises the steps of carrying out a first treatment on the surface of the Δt represents the time interval from the t-1 period to the t period.
According to the indoor comfort level requirement of the user and the heat obtaining and radiating characteristics of the rooms, the second load constraint condition of the daily optimization model, which is determined by clustering the indoor environment into n types of rooms based on the user comfort level quantification index, is as follows:
indoor illuminance constraint:
E min (n,t)≤E(n,t)≤E max (n,t)；
Wherein E is min (n, t) is the minimum value of the indoor illuminance of the nth type room in the t period; e (E) max (n, t) is the maximum value of the indoor illuminance; e (n, t) is the indoor illuminance of the nth room in the nth period.
Indoor temperature constraint:
in the method, in the process of the invention,a minimum value of the indoor temperature of the nth type room in the t period; />Maximum value of indoor temperature of the nth room in t time period; t (T) room (n, t) is the room temperature of the nth class room at the end of the t period.
Indoor CO 2 Concentration constraint:
N min (n,t)≤N(n,t)≤N max (n,t)；
wherein N is min (n, t) is the indoor CO of the nth room of the t period 2 Minimum value of concentration; n (N) max (n, t) is the indoor CO of the nth room of the t period 2 Maximum value of concentration; n (N, t) is the indoor CO of the nth room at the end of the t period 2 Concentration values.
Adjustable load start time constraint:
wherein t is d The starting time of the adjustable load;the set earliest starting time is set; />Is the set latest starting time.
Adjustable load adjustment constraint:
wherein M is IL (t) the number of load cells adjusted for the optimization period,the maximum load unit number adjustable for the period t.
The adjustable load referred to in this embodiment refers to a load that can be partially adjusted under the direction of the price of electricity or other stimulus signal issued by the system. The mapping between the adjustable load and the load power before and after the integrated demand response adjustment can be expressed as:
Wherein: p (P) IL (t) is the load power adjusted by the integrated demand response for period t;load power not adjusted by the integrated demand response for the ith user of period t; />The power variation quantity regulated by the comprehensive demand response for the ith user in the t period; n (N) IL To the number of users engaged in the adjustable load response.
When an adjustable load is scheduled, its adjustable power is constrained by the maximum curtailed power.
Wherein:maximum power for the ith user; ΔP i IL And (t) is the adjustable power of the regulating load in one regulating period.
And B3, constructing an intra-day optimization model according to the second objective function and the second constraint condition.
In this embodiment, the obtaining the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source based on the daily scheduling plan and the real-time electricity price of the different micro source start-stop, the time-shift load and the energy storage in step 2 and the pre-built daily optimization model includes:
reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters;
and optimizing the initialization parameters by rolling at a second time interval, and introducing real-time electricity price as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day. Further reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters, and specifically comprising the following steps:
The day-ahead optimization module reads the day-ahead plan and initializes the day-ahead optimization algorithm parameters, and then triggers the day-ahead optimization module to make a new round of day-ahead plan. And a day-ahead optimization algorithm establishes a start-stop plan of a day-ahead gas boiler, a ground source heat pump, a power distribution network and a photovoltaic, and a scheduling plan of time-shifting load and energy storage according to initial values of all parameters. Further, the initialization parameters are optimized by rolling at a second time interval, and real-time electricity price is introduced as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day, which comprises the following steps:
the daily optimization module reads a daily schedule and initializes daily optimization algorithm parameters, then triggers the daily optimization module to make a new round of daily schedule, and the daily optimization module adopts a rolling optimization method to determine a current day ultra-short period controllable power output and adjustable load scheduling schedule, and reads the new round of daily schedule after completing scheduling tasks of 96 time periods of the current day; the daily optimization is rolling optimization with real-time electricity price as market excitation signal at 15 min intervals based on daily optimization result. The photovoltaic power generation power and the load power of 4 time periods within 1 hour in the future are predicted 15 minutes in advance, namely, the source load value of the [ t+1, t+4] time period is predicted in the time period assuming that the current time period is t. The multiple optimization targets of economy and comfort are comprehensively considered, the controllable power supply, the adjustable load and the energy storage in the [ t+1, t+4] period are optimized, but only the controllable power supply output, the adjustable load power and the energy storage power in the t+1 period are determined, and the like.
A building source load storage cooperative operation method for energy storage of a ground source heat pump further comprises real-time correction of unbalanced power based on daily optimization load.
The corrected unbalanced power mentioned in this embodiment is as follows:
obtaining unbalanced electric power based on the difference value between the real-time load of the building energy center and the daily optimized load;
and correcting the unbalanced electric power in real time based on the daily optimized load to determine a corrected adjustable load plan.
Unbalanced electric power generated during real-time operation is corrected by an adjustable load. The building energy center calculates distributed photovoltaic output according to the monitored real-time temperature, irradiance and other data, and calculates unbalanced power according to the real-time load information. The real-time unbalanced power is the difference between the real-time payload and the daily optimized payload, and can be expressed as
ΔP L (t)＝P L (t)-P pv (t)-∑ i P i (t)；
Wherein: ΔP L (t) calculating unbalanced power of building energy centers running in real time in a period; p (P) L (t) is the real-time load of the building energy center; p (P) pv (t) is the real-time output of the photovoltaic; sigma (sigma) i P i And (t) represents electrical power taken from other facilities, such as the grid, gas turbines (if any), energy storage. Wherein the variable units are kW. ΔP L (t) < 0 indicates that the daily optimization result meets the load demand and the electric quantity is surplus, and a small amount of waste light or power grid is required to be returned; ΔP L (t) > 0 indicates that the daily optimization result cannot meet the load demand, and the load needs to be reduced appropriately.
And 3, cooperatively optimizing the operation of building source load storage on the basis of the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
Example 2:
the invention also provides a building source load storage cooperative operation system for energy storage by the ground source heat pump based on the same invention conception, as shown in fig. 5, comprising:
the day-ahead plan generation module is used for obtaining day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of the building energy center in combination with a pre-built day-ahead optimization model;
the intra-day plan generation module is used for obtaining an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in the micro source based on the pre-day scheduling plans of the different micro source start-stop, time-shifting load and energy storage and a real-time electricity price in combination with a pre-built intra-day optimization model;
and the collaborative optimization module is used for collaborative optimization of the operation of the building source load storage based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
The building source load storage collaborative operation system with the ground source heat pump for energy storage further comprises a day-ahead optimization model construction module, wherein the day-ahead optimization model construction module is used for constructing a day-ahead optimization model.
The day-ahead optimization model construction module is specifically used for:
the relation determining submodule is used for determining conversion relation of two sides of energy supply and demand based on micro-source, load and energy storage data of the building energy center and an energy system coupling matrix;
the first objective function submodule is used for taking the minimum energy cost meeting the conversion relation as a first objective function;
the first constraint sub-module is used for constraining the starting moment of the time-shifting load translation and constraining the time-shifting load quantity as a first constraint condition;
and the day-ahead module construction submodule is used for constructing a day-ahead optimization model according to the first objective function and the first constraint condition.
The relation determination submodule is specifically configured to:
firstly, the micro source, load and energy storage data of the building energy center mentioned in the embodiment are described in detail:
the basic structure of the energy concentrator of the building energy center is shown in fig. 2, wherein the energy supply unit comprises distributed photovoltaics, a ground source heat pump and a gas boiler, the energy storage unit comprises heat storage, electricity storage and gas storage, and the load is divided into electric load, thermal load and cold load.
(1) Basic data of equipment such as a ground source heat pump, a gas boiler, a distributed photovoltaic and the like in an energy supply unit are acquired, and the basic data include but are not limited to:
Ground source heat pumps, such as refrigeration capacity and heating capacity, refrigeration and heating efficiency, electric power, temperature, flow, pressure, ramp rate, and the like;
gas boilers such as heating capacity, heat efficiency, evaporation capacity, pressure, temperature, climbing rate, etc.;
distributed photovoltaics, such as capacity, efficiency, photovoltaic module parameters, and the like.
(2) Basic data of equipment such as a heat storage water tank, a storage battery and the like in the energy storage unit is acquired, including but not limited to:
a thermal storage tank such as an amount of stored heat or cold, efficiency, pressure, temperature, ramp rate, etc.;
storage batteries such as capacity, efficiency, operating temperature, ramp rate, maximum daily charge and discharge times, etc.;
air reservoirs, such as air reservoir, pressure, flow, etc.
(3) Obtaining side-of-charge data, including but not limited to:
heat load or cold load such as indoor temperature and humidity, effective area, house thermal resistance, etc.;
the electrical load, e.g., the time-shifted load amount, the adjustable load amount, the total power of the powered device, etc.
The second is the introduction of the construction of the conversion relation between the two sides of energy supply and demand:
establishing a conversion matrix for connecting the two sides of energy supply and demand, comprising:
according to the conversion and distribution process of various energy sources such as electricity, heat and the like which are schematically shown in the energy concentrator, the conversion relationship of the two sides of the energy source supply and demand is obtained
L(t)＝C(t)P(t)；
Wherein: l (t) represents the load side, specifically L (t) = [ L e (t) L h (t) L c (t)] T The method comprises the steps of carrying out a first treatment on the surface of the P (t) represents an energy supply side, specifically, P (t) = [ P ] dg P pv P se P bo P hp P sc P sh ] T The method comprises the steps of carrying out a first treatment on the surface of the C (t) representsThe coupling matrix of the energy system in the energy center has coefficients related to energy conversion efficiency, scheduling coefficients and constraint conditions.
Wherein: η (eta) tr Indicating the transformation efficiency of the distribution transformer, u 1 (t) is the load factor of the t period; η (eta) pv Represents the inversion efficiency of photovoltaic power generation, u 2 (t) is the force coefficient of the t period; η (eta) se For the conversion efficiency of the accumulator, u 3 (t) is the battery charge or discharge coefficient; η (eta) bo Represents the heating efficiency of the gas boiler, m 1 (t) represents the heating load coefficient of the gas boiler in the period t, and 0 is taken when the gas boiler is not heated; η (eta) hph Represents the heating efficiency of the heat pump unit, m 2 (t) represents a heating load factor of the heat pump; η (eta) sh For the conversion efficiency of the heat storage water tank, m 3 (t) is a storage or release coefficient; η (eta) hpc Represents the refrigerating efficiency of the heat pump unit, n 1 (t) represents a refrigeration load factor of the heat pump; η (eta) a Indicating the efficiency of the common air conditioning system, n 2 (t) represents a common air conditioner refrigeration load factor, and 0 is taken when no other air conditioner is refrigerated; n is n 3 (t) is a storage or release coefficient; η (eta) sc Is the conversion efficiency of the heat storage water tank
The plurality of coefficients mentioned in C (t) are related to the respective device operating conditions and load rates, each coefficient being constant while the device operating conditions and load rates remain unchanged. Each element in P (t) is the rated power or installed capacity of the device under a specific operating condition.
Determining constraints of the unified mathematical model, comprising:
setting unit operation constraints of a heat pump unit, photovoltaic power generation, a gas boiler, a heat storage water tank, a storage battery and the like;
and setting the power interaction constraint of the public distribution network and the building energy center.
The first objective function submodule is specifically configured to:
wherein, C is the energy cost in the optimization period before the day; c (C) f (t) is the t period outsourcing fuel cost; c (C) dg (t) is the t period grid interaction cost; c (C) om (t) is a t-period operation maintenance cost; c (C) cs (t) the start-stop cost of the time period controllable unit; c (C) RL And (t) adjusting the cost for the t period load. In the formula, the unit of each variable is ten thousand yuan.
The objective function of the day-ahead optimization model may be represented by decision variables as
min C＝f(U bo ,U pv ,U hp ,U dg ,P TL )；
Wherein: u (U) bo 、U pv 、U hp And U dg Respectively represents the start-stop state and the stop state of a gas boiler, a photovoltaic ground source heat pump and a power distribution network, P TL Is the time-shiftable load.
The first constraint submodule is specifically configured to:
the load constraint condition of the day-ahead optimization model is that
Time-shift load translation start time constraint:
wherein t is 1 Is the starting time of the time-shift load;is the set lower limit time; />The upper limit time is set; Δt (delta t) TL Representing the time-lapse load duration. In the formula, each variable unit is hour.
Time-shiftable load amount constraint:
In the method, in the process of the invention,the number of maximum load units time-shifted for the period t; m is M TL (t) is the number of load units shifted in the optimization period.
The time-shifting load mentioned in the embodiment refers to the load of which the power consumption time period is controlled by the system comprehensive demand response scheduling center under the condition that the power consumption time of the load is unchanged. The mapping relationship between the time-shifted load and the time-shifted load power which is not regulated by the integrated demand response can be expressed as
Wherein: p (P) TL (t) time-shifted load adjusted by the integrated demand response for time period t;time-shifted load power for the ith user of period t, not adjusted by the integrated demand response; />The load power of the i-th user in the t period is accessed and removed after the comprehensive demand response adjustment; n (N) TL To the number of users participating in the time-lapse load response; t (T) in 、T out The time shift load switching-in period and the time shift load switching-out period are respectively carried out; />Load power shifted in for the t period; />Load power shifted out for period t.
The day-ahead plan generation module is specifically used for:
the method comprises the steps of optimizing and predicting building integrated photovoltaic power generation power and load time-by-time power of the next day in the day, calculating market excitation signals such as time-of-use electricity price and gas price, optimizing the start-stop, time-shifting load and energy storage state of a controllable power supply at intervals of 1 hour, and making a start-stop plan of a gas boiler, a ground source heat pump, a power distribution network and photovoltaics of the next day and a scheduling plan of the time-shifting load and energy storage according to initial values of all parameters by a day optimization algorithm.
The building source load storage collaborative operation system with the ground source heat pump for energy storage further comprises a daily optimization model construction module, wherein the daily optimization model construction module is used for constructing a daily optimization model.
The daily optimization model construction module is specifically as follows:
a second objective function construction sub-module for;
a second constraint sub-module for: an intra-day model building sub-module for: the second objective function construction submodule is specifically configured to:
the second objective function of the intra-day phase optimization model is:
economic objectives:
wherein C is 1 Optimizing the energy consumption cost in the period for the day;the cost of outsourcing fuel for the t period; />The interaction cost of the power grid in the t period; />Operating maintenance cost for the period t; />The cost is adjusted for t-period load. In the formula, the unit of each variable is ten thousand yuan.
The objective function of the intra-day optimization model can be represented by decision variables as
min F＝g(P bo ，P pv ，P hp ，P dg ，P se ，P sh ，P sc )；
Wherein: f represents a comprehensive optimization target value, P bo 、P pv 、P hp 、P dg 、P se 、P sh And P sc Respectively representing heat storage (or cold storage) power of a gas boiler, a photovoltaic, a ground source heat pump, a power distribution network, a storage battery and a heat storage water tank.
Comfort level target:
the lighting comfort index is characterized by an indoor illuminance that varies within a user acceptable range.
Wherein A is 1 (n, t) is the lighting comfort of the nth class room for period t; e (E) s For the indoor standard illuminance, the unit is lx.
Thermal comfort is characterized by an indoor temperature that varies within a range of allowable values set by a user.
Wherein A is 2 (n, t) is the temperature comfort of the nth class room for period t; t (T) s Is the indoor standard temperature, the unit is the temperature, T room (n, t) represents the indoor temperature of the nth class room at the end of the t period.
CO for air quality comfort 2 Concentration characterization, indoor CO 2 The concentration is atThe allowable value range set by the user is changed.
Wherein A is 3 (n, t) air quality comfort for a class n room for a period t; n (N) s Is indoor standard CO 2 Concentration in ppm, N (N, t) represents the indoor CO of the nth class room at the end of the t period 2 Concentration values.
A(n，t)＝aA 1 (n，t)+bA 2 (n，t)+cA 3 (n，t)；
Where a (n, t) is the comfort level of the nth class room for the period t. Wherein a, b, c are set according to the preference of the user and satisfy a+b+c=1, herein taken a=b=c; a is that 2 (n, t) is the temperature comfort of the nth class room for period t; a is that 1 (n, t) is the lighting comfort of the nth class room for the t period.
Comprehensive optimization targets:
the daily optimization considers the dual targets of economy and comfort, the multi-target optimization is converted into single-target optimization solution by adopting a weighted aggregation method, and the comprehensive optimization target is
min F＝wC 1 +y(1-w)(1-A)；
Wherein: f represents a comprehensive optimization target value, and w is the weight between the economical efficiency and the comfort level set by a user according to actual demands; y is the difference used to balance the two dimensions.
The second constraint submodule is specifically configured to: :
the user comfort quantization index is designed in this embodiment as follows:
the illumination comfort level, the thermal comfort level and the indoor air quality comfort level are three main factors influencing the user comfort level, and the corresponding quantization indexes are illumination, temperature and humidity and CO 2 Concentration.
The power of the controllable lighting equipment can be continuously adjusted within a certain range, namely the indoor illuminance changes along with the increase and decrease of the number of the lighting equipment, and the illuminance mathematical model can be expressed as
Wherein: e (n, t) is the indoor illuminance of the nth room in the nth period, and the unit is 1x; b (n) and m (k) respectively represent the number of indoor light sources in the nth room and the luminous flux of each k light sources, and the units are respectively the class and 1m; x, Y is the utilization coefficient and maintenance coefficient of the light source, and represents the effective utilization degree and the light loss degree of the luminous flux; s (n) represents the illuminated area of the nth type room in m 2 。
For air conditioning equipment, establishing a mathematical model of the relation between air conditioning energy consumption and indoor and outdoor temperatures as
Wherein: t (T) room (n，t)、T out (n, t) respectively representing the indoor temperature and the outdoor temperature of the nth room at the end of the t period, wherein the units are DEG C; r is R eq The equivalent thermal resistance of the room is shown as omega; m is M air Representing indoor air quality; c (C) P The specific heat of air is expressed as J/(kg.K); q (n, t) represents heat transferred from the room by the nth type room air conditioning system in the t period, and the unit is J; t (T) room (n, t-1) represents the indoor temperature of the nth class room at the end of the t-1 period.
The indoor air quality is characterized by the indoor CO2 concentration, which is required to vary within a range of allowable values set by the user. To maintain indoor CO 2 The concentration is stable, a certain fresh air quantity is required to be provided for the room, and the indoor CO 2 The mathematical relationship between the concentration and the fresh air quantity is expressed as
N (N, t-1) represents the indoor CO of the nth class room at the end of the t-1 period 2 Concentration value, B represents fresh air flow, and the unit is m 3 /s；N W Representing CO in outdoor air 2 The concentration of the water in the water is higher,representing indoor CO of an nth class room 2 Is a rate of production of (1); v represents the total volume of the room, unit m 3 The method comprises the steps of carrying out a first treatment on the surface of the Δt represents the time interval from the t-1 period to the t period.
According to indoor comfort level requirement of users and heat obtaining and radiating characteristics of rooms, the rooms are clustered into n types of rooms
The load constraint condition of the daily optimization model is determined based on the user comfort quantitative index as follows:
Indoor illuminance constraint:
E min (n，t)≤E(n，t)≤E max (n，t)；
wherein E is min (n, t) is the minimum value of the indoor illuminance of the nth type room in the t period; e (E) max (n, t) is the maximum value of the indoor illuminance.
Indoor temperature constraint:
in the method, in the process of the invention,a minimum value of the indoor temperature of the nth type room in the t period; />Maximum value of indoor temperature of the nth room in t time period; t (T) room (n, t) is the room temperature of the nth class room at the end of the t period.
Indoor CO 2 Concentration constraint:
N min (n,t)≤N(n,t)≤N max (n,t)；
wherein N is min (n, t) is the indoor CO of the nth room of the t period 2 Minimum value of concentration; n (N) max (n, t) is the indoor CO of the nth room of the t period 2 Maximum concentration.
Adjustable load start time constraint:
wherein t is d The starting time of the adjustable load;the set earliest starting time is set; />Is the set latest starting time.
Adjustable load adjustment constraint:
/>
wherein M is IL (t) the number of load cells adjusted for the optimization period,the maximum load unit number adjustable for the period t.
The adjustable load referred to in this embodiment refers to a load that can be partially adjusted under the direction of the price of electricity or other stimulus signal issued by the system. The mapping relationship between the adjustable load and the load power before and after the integrated demand response adjustment can be expressed as
Wherein: p (P) IL (t) is the load power adjusted by the integrated demand response for period t;load power not adjusted by the integrated demand response for the ith user of period t; />The power variation quantity regulated by the comprehensive demand response for the ith user in the t period; ) IL To the number of users engaged in the adjustable load response.
When an adjustable load is scheduled, its adjustable power is constrained by the maximum curtailed power.
Wherein:maximum power for the ith user; p (P) IL And (t) is the load power adjusted by the integrated demand response in period t.
The intra-day plan generation module is specifically configured to:
the day-ahead optimization module reads the day-ahead plan and initializes the day-ahead optimization algorithm parameters, and then triggers the day-ahead optimization module to make a new round of day-ahead plan.
The daily optimization is rolling optimization with real-time electricity price as market excitation signal at 15 min intervals based on daily optimization result. The photovoltaic power generation power and the load power of 4 time periods within 1 hour in the future are predicted 15 minutes in advance, namely, the source load value of the [ t+1, t+4] time period is predicted in the time period assuming that the current time period is t. The multiple optimization targets of economy and comfort are comprehensively considered, the controllable power supply, the adjustable load and the energy storage in the [ t+1, t+4] period are optimized, but only the controllable power supply output, the adjustable load power and the energy storage power in the t+1 period are determined, and the like.
The building source load storage cooperative operation system with the ground source heat pump for energy storage further comprises a power correction module. The power correction module is specifically configured to:
obtaining unbalanced electric power based on the difference value between the real-time load of the building energy center and the daily optimized load;
and correcting the unbalanced electric power in real time based on the optimized electric load in the day, and determining a corrected adjustable load plan.
Unbalanced electric power generated during real-time operation is corrected by an adjustable load. The building energy center calculates distributed photovoltaic output according to the monitored real-time temperature, irradiance and other data, and calculates unbalanced power according to the real-time load information. The real-time unbalanced power is the difference between the real-time payload and the daily optimized payload, and can be expressed as
ΔP L (t)＝P L (t)-P pv (t)-∑ i P i (t)；
Wherein: ΔP L (t) calculating unbalanced power of building energy centers running in real time in a period; p (P) L (t) is the real-time load of the building energy center; p (P) pv (t) is the real-time output of the photovoltaic; sigma (sigma) i P i And (t) represents electrical power taken from other facilities, such as the grid, gas turbines (if any), energy storage. Wherein the variable units are kW. ΔP L (t)<0 indicates that the daily optimization result meets the load demand and the electric quantity is surplus, and a small amount of light is abandoned or the power grid is reversely transmitted; ΔP L (t)>0 indicates that the daily optimization result cannot meet the load demand, and a proper load reduction is required.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computing core and control core of the terminal and adapted to implement one or more instructions, specifically adapted to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of the architecture source load store co-operating method of ground source heat pump energy storage in the above embodiments.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of the method for collaborative operation of building source charge storage for ground source heat pump energy storage in the above embodiments.
It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (15)
1. The building source load storage cooperative operation method for the energy storage of the ground source heat pump is characterized by comprising the following steps of:
Acquiring day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of a building energy center in combination with a pre-constructed day-ahead optimization model;
based on the different micro-source start-stop, time-shifting load and energy storage day-ahead scheduling plans and real-time electricity price, and a pre-built day-ahead optimization model, obtaining a controllable unit output, an adjustable load and energy storage day-ahead scheduling plan in the micro-source;
and the operation of the building source load storage is cooperatively optimized based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
2. The method of claim 1, wherein the construction of the day-ahead optimization model comprises:
determining conversion relations of two sides of energy supply and demand based on micro-source, load and energy storage data of the building energy center and an energy system coupling matrix;
the minimum energy cost meeting the conversion relation is used as a first objective function;
taking the time-shift load translation starting moment constraint and the time-shift load quantity constraint as a first constraint condition;
and constructing a day-ahead optimization model by the first objective function and the first constraint condition.
3. The method of claim 2, wherein the construction of the intra-day optimization model comprises:
-determining a second objective function from said economy and comfort satisfying a conversion relation;
with indoor illuminance constraint, indoor temperature constraint and indoor CO 2 Concentration constraint, adjustable load starting time constraint and adjustable load adjustment quantity constraint are second constraint conditions;
and constructing an intra-day optimization model by the second objective function and the second constraint condition.
4. The method of claim 2, wherein the first objective function is represented by the formula:
wherein, C is the energy cost in the optimization period before the day; c (C) f (t) is the t period outsourcing fuel cost; c (C) dg (t) is the t period grid interaction cost; c (C) om (t) is a t-period operation maintenance cost; c (C) cs (t) the starting and stopping cost of the time period controllable unit; c (C) RL And (t) adjusting the cost for the t period load.
5. A method according to claim 3, wherein the second objective function is represented by the formula:
minF＝wC 1 +y(1-w)(1-A)；
wherein w is the weight between the economy and the comfort level set by the user according to the actual demand; y is the difference between the two dimensions used for balancing; c (C) 1 Optimizing the energy consumption cost in the period for the day; a is comfort level; f is the comprehensive optimization target value.
6. The method of claim 1, wherein the micro-source, load and energy storage data of the building energy center comprises: ground source heat pump electric power, gas boiler pressure, gas boiler temperature, distributed photovoltaic efficiency, photovoltaic module parameters, heat storage tank climbing rate, storage battery maximum daily replay times, gas storage tank gas storage capacity, indoor temperature and humidity, time-shifting load capacity, adjustable load capacity and electric equipment total power.
7. The method of claim 2, wherein the conversion relationship is calculated by:
L(t)＝C(t)P(t)；
wherein L (t) is the load side; p (t) is the energy supply side; c (t) is a coupling matrix of an energy system inside the energy center.
8. The method of claim 1, wherein the generating the day-ahead schedule of different micro-source start-stop, time-shift load and energy storage based on the micro-source, load and energy storage data of the building energy center in combination with a pre-built day-ahead optimization model comprises:
and optimizing building integrated photovoltaic power generation power, load time-by-time power, counted time-of-use electricity price and gas price at a first time interval based on micro-source, load and energy storage data of the building energy center to obtain a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage of the next day.
9. The method of claim 1, wherein the obtaining the daily schedule of controllable unit output, adjustable load and stored energy power in the micro-sources based on the daily schedule of different micro-sources start-stop, time-shift load and stored energy and the real-time electricity price in combination with a pre-built daily optimization model comprises:
reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters;
And optimizing the initialization parameters by rolling at a second time interval, and introducing real-time electricity price as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day.
10. The method of claim 9, wherein the method further comprises:
obtaining unbalanced electric power based on the difference value between the real-time load of the building energy center and the daily optimized load;
and correcting the unbalanced electric power in real time based on the daily optimized load to determine a corrected adjustable load plan.
11. The utility model provides a building source lotus stores up collaborative operation system of ground source heat pump confession energy storage which characterized in that includes:
the day-ahead plan generation module is used for obtaining day-ahead scheduling plans of different micro-source start-stop, time-shifting load and energy storage based on micro-source, load and energy storage data of the building energy center in combination with a pre-built day-ahead optimization model;
the intra-day plan generation module is used for obtaining an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in the micro source based on the pre-day scheduling plans of the different micro source start-stop, time-shifting load and energy storage and a real-time electricity price in combination with a pre-built intra-day optimization model;
And the collaborative optimization module is used for collaborative optimization of the operation of the building source load storage based on the daily scheduling plan of the controllable unit output, the adjustable load and the energy storage power in the micro source.
12. The system of claim 11, wherein the day-ahead plan generation module is specifically configured to:
and optimizing building integrated photovoltaic power generation power, load time-by-time power, counted time-of-use electricity price and gas price at a first time interval based on micro-source, load and energy storage data of the building energy center to obtain a day-ahead scheduling plan of different micro-source start-stop, time-shifting load and energy storage of the next day.
13. The system of claim 11, wherein the intra-day plan generation module is specifically configured to:
reading the day-ahead scheduling plans of the different micro-source start-stop, time-shifting load and energy storage and initializing parameters;
and optimizing the initialization parameters by rolling at a second time interval, and introducing real-time electricity price as a market excitation signal to obtain an intra-day scheduling plan of controllable unit output, adjustable load and energy storage power in a micro source at a second time of the next day.
14. A computer device, comprising: one or more processors;
The processor is used for storing one or more programs;
a method of co-operating with building source charge storage for ground source heat pump-fed storage according to any one of claims 1 to 10, when said one or more programs are executed by said one or more processors.
15. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements the method for collaborative operation of a ground source heat pump for energy storage of a building source load according to any one of claims 1 to 10.
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