CN108629470A - System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization - Google Patents
System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization Download PDFInfo
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Abstract
The present invention is directed to the Multi-function mutual complement system under energy Background of Internet, and system operator and user are considered as two different behavioral agents, based on the framework for concentrating the energy hinge of interconnection to build cogeneration Multi-function mutual complement system.To plurality of energy supplies in garden, energy storage, with can equipment analyze, establish the fining energy flow model of various kinds of equipment, and count and users'comfort formulates detailed management and running strategy to electric vehicle, household electrical appliance, hot water load, air thermic load.Establish the leader-followers games model of Multi-function mutual complement system, model using minimum user can cost, maximize operator's income as target, consider it is above-mentioned for, store, can equipment it is mutually coordinated.By simulating, verifying, institute's established model of the present invention, which can reach, fully to be excavated user's independence, effectively takes into account common interest demand, realizes that energy network interconnects the purpose mutually helped.
Description
Technical Field
The invention belongs to the field of multi-energy complementary system operation, and relates to an operation control strategy for operators and users.
Background
Increasing climate deterioration and energy crisis have put new demands on the promotion of energy revolution and the improvement of energy utilization efficiency. According to statistics, at present, China has nearly 2000 development areas of national level, provincial level and the like, the energy utilization requirement space in a park is huge, the regulation and control and the management of loads have higher flexibility and controllability, and the park type basic energy Internet constructed by taking the park as a unit is combined with the actual conditions of local resources and loads of the park, so that a way is provided for realizing the efficient complementary utilization of various energy sources.
The extensive research on the combined heat and power system using a micro gas turbine as a core device by scholars at home and abroad in the year mainly has two problems:
(1) the consideration of the details of electric and thermal load scheduling is lacked, and the method is not beneficial to subsequent popularization.
(2) Currently, there are many research results on centralized optimization dominated by a single subject, and under the competitive and open market environment, various operating subjects including power selling companies, agencies, microgrid operators, users, and the like are popularized in the power system. Because the benefit requirements of each benefit subject are different, the centralized optimization is difficult to take the benefits of multiple subjects into consideration, and the distributed optimization is an effective method for solving the problems from the viewpoint of satisfying the dynamic balance of the benefits of the multiple subjects and based on higher flexibility and adaptability. However, the existing research results focus on optimization from the perspective of operators, neglect the benefit requirements of users, and lead to low participation degree. However, with the improvement of the power market and the development of communication and metering technologies, the user individuals are more intelligent and autonomous, and are no longer simply the policy acceptors of the operators, and the users can also participate in the optimized operation of the system as one of the main subjects. The operators and the users serve as different decision-making subjects, the interest competition relationship is more complex, the interaction mechanism is researched to realize the active interaction between the energy source side and the load side, and the method is an effective way for realizing the optimization of the energy efficiency and the environmental benefit.
Disclosure of Invention
On the energy demand side, household energy consumption occupies a considerable proportion, and aiming at a multi-energy complementary system under the energy internet background, the invention regards system operators and users as two different behavior main bodies and builds a framework of the combined heat and power multi-energy complementary system based on a centrally interconnected energy hub. Around this framework, multiple energy supply, energy storage, energy consumption equipment in the garden are analyzed, establish the energy flow model that becomes more meticulous of all kinds of equipment to consider that user's comfort level makes detailed scheduling operation strategy to electric automobile, domestic appliance, hot water load, air heat load. A master-slave game model of the multi-energy complementary system is established, the model aims at minimizing the energy cost of users and maximizing the income of operators, and the mutual coordination of the energy supply, storage and utilization devices is comprehensively considered.
In order to achieve the purpose, the technical scheme provided by the invention is that the household type multi-energy complementary system energy management and optimized operation based on the non-cooperative game is characterized by comprising the following steps:
step 1: the method comprises the following steps of building a framework of a combined heat and power household type multi-energy complementary system based on energy hubs which are connected in a centralized manner, and analyzing the operation mode of the system:
(1) and the operator prediction center predicts the air temperature and the photovoltaic unit output in each time period of the next day in the day.
(2) At the day, operators pre-schedule gas turbine and gas boiler outputs based on the customer's heat load requirements. For the electric load, the demand is preferentially met by the photovoltaic and gas turbine units, an electric power purchase contract is signed with a superior power grid, the electric power purchase quantity of each time period is determined, and the real-time electricity price of each time period in the next day is issued to a user. In real-time operation, the operator may also sell (purchase) electrical energy to the real-time market when the system power supply is surplus (insufficient).
(3) In the day, a Home Energy Management System (HEMS) installed inside a user family calculates the comfort level of the user according to the electricity price information issued by an operator, automatically selects the optimal power utilization strategy of various household appliances and controls the switching-on and switching-off of various translatable loads; on the other hand, on the premise of meeting the temperature comfort degree, the heat load in each time period is optimized.
Step 2: establishing an output model and a cost model of the micro gas turbine with the operation requirements of related equipment as constraint conditions;
and step 3: considering comfort degree constraint of a user, converting starting time of the translatable load to be optimized into a 0,1 vector representing the starting and stopping states of the translatable load, and accordingly making an operation control strategy of the translatable load; and establishing a PMV index to quantify the temperature comfort level, and establishing a model of the space heat load and the hot water load by taking the temperature comfort level as a constraint.
And 4, step 4: establishing a master-slave game model between an operator and a user, establishing an operator income model and a user income model, dividing the model into an upper sub-module and a lower sub-module, taking the maximum operator income as an objective function at the upper layer, taking the minimum user payment cost as the objective function at the lower layer under the premise of ensuring the self comfort degree constraint, and combining a chaotic fish school algorithm and a YALMIP tool box in an MATLAB to solve to obtain a strategy set of game parties at a balance point.
Drawings
1. FIG. 1 is a diagram of a multi-energy complementary system architecture
2. FIG. 2 is an optimization flow chart
3. FIG. 3 is a hot water load demand of an individual residential user during a day
4. FIG. 4 is a flexible electrical load parameter
5. FIG. 5 shows parameters of thermal resistance, temperature, specific heat capacity, storage tank volume, and water density
6. FIG. 6 shows the optimization results of hot water temperature and room temperature
7. FIG. 7 is operator defined real-time electricity prices
8. FIG. 8 is a user rigid load
9. FIG. 9 is a translatable load optimization result.
Detailed Description
Step 1: establishing a framework for building a cogeneration and multi-energy complementary system based on a centralized interconnected energy hub, and analyzing the operation mode of the system:
the operation mode is as follows:
(1) and the operator prediction center predicts the air temperature and the photovoltaic unit output in each time period of the next day in the day.
(2) At the day, operators pre-schedule gas turbine and gas boiler outputs based on the customer's heat load requirements. For the electric load, the demand is preferentially met by the photovoltaic and gas turbine units, an electric power purchase contract is signed with a superior power grid, the electric power purchase quantity of each time period is determined, and the electricity price information of each time period in the next day is issued to the user. In real-time operation, the operator may also sell (purchase) electrical energy to the real-time market when the system power supply is surplus (insufficient).
(3) In the day, a Home Energy Management System (HEMS) installed inside a user family automatically selects an optimal power utilization strategy of various household appliances and controls the switching-on and switching-off of various translatable loads according to energy price information (including real-time electricity price and heating price) issued by an operator, the comfort level of the user is considered, and the energy consumption strategy is controlled; on the other hand, on the premise of meeting the temperature comfort degree, the heat load in each time period is optimized.
Step 2: establishing a micro gas turbine output model and a cost model taking the operation requirement as a constraint condition:
the micro gas turbine realizes the integration of power generation and heat supply (heating and hot water supply) processes by utilizing high-temperature waste heat generated in the power generation process.
(1) Force model
The mathematical model describing the gas turbine output is:
（1）
in the formula, QMT(t)The exhaust waste heat quantity of the gas turbine at the moment t;is the heat dissipation loss coefficient of the gas turbine; pe(t) is the electric power output by the gas turbine at time t; qhe(t) the heating capacity provided by the waste heat of the flue gas of the gas turbine at the moment t; kheThe heating coefficient of the bromine refrigerator is shown; vMTThe amount of natural gas consumed by the gas turbine; Δ t is the operating time of the gas turbine; l is the heat value of natural gas, and is 9.7kWh/m3。
(2) Cost model
The operating cost of the gas turbine is mainly the purchase gas cost, and the expression of the gas cost is as follows:
（2）
wherein rgas is the unit price of the natural gas, and R is the heat value of the natural gas.
(3) Constraint conditions
Capacity constraint of gas turbine plant:
（3）
ramp rate constraint for gas turbine
（4）
And step 3: establishment of household appliances, hot water load and air heat load considering user comfort
And (3) making a detailed scheduling operation strategy:
the electrical load model and control strategy are as follows:
it can be classified into two categories according to load response characteristics: 1) The uninterruptible load mainly refers to equipment such as lighting, a refrigerator and a television which meet basic daily requirements of people, the requirements of users are approximately rigid, once the electricity utilization time is adjusted, the comfort of the users can be seriously affected, and the uninterruptible load is insensitive to the change of electricity price. 2) The load can be translated, the load can not be interrupted after being started, but the start can be delayed, the shape of the load is not influenced, the starting time of the load generally depends on the electricity utilization mode and the living habit of a user, the load is sensitive to the change of electricity price, and the load mainly comprises a washing machine, a dryer, a dish washing machine and the like. The purpose of reducing the electricity consumption cost while meeting the comfort level of a user can be achieved by planning the starting time of the load. Therefore, the invention mainly takes the translatable load as an optimization object. The electrical characteristics of a translatable load are modeled as:
for translatable loadsL iLet it be the number of segments in continuous operationH iThe optional operation time period is [ 2 ]tstart i，tend i]. The variable to be optimized for the translatable load is its actual start timet i∈[tstart i,tend i-H i+1]Converting the vector into a start-stop state vector in an optimization period (24 h)xt i E {0,1}, respectivelytIn the first periodiThe class can translate the off, on state of the load, so that the total load amount per time period can be determined:
（5）
wherein NSAL is the total number of types of translatable loads;P t iis composed oftIn the first periodiPower of the class translatable load;P t bis composed oftThe time period is not the power of the translatable load,P t cis composed oftThe time period may reduce the power of the load.
The translatable load needs to meet conditions such as an uninterruptible constraint, an operating power constraint and the like, which are respectively expressed as follows:
(1) an uninterruptible constraint:
（6）
in the formula, tau is the starting time after the i-type load optimization. The constraints indicate that once the load is turned on, at least a continuous operation H is requirediA plurality of time periods.
(2) And (3) constraint of operating power:
（7）
in the formula, pi NRated power for a load of type i, pi minAnd pi maxRespectively, the minimum and maximum operating power for the class i load. The above constraints indicate that each type of load has a certain limit for the consumption of electric energy, and that for machines such as washing machines, dishwashers and the like, the consumption of electric energy reaches a nominal value once the machine has started to operate, and the power is 0 when the machine has stopped to operate.
(3) Transferable time constraints:
（8）
it should be noted that although the load characteristics and electricity usage habits of different households are different, the user can set the service time range of the electric appliance according to the own requirements, so that the personalized requirements of the user are met.
The air thermal load model considering comfort is as follows:
according to the given relationship between the outdoor environment temperature, the indoor temperature and the heating power required for maintaining the room temperature and the corresponding parameter setting, the constraint relationship among the variables is as shown in the formula (9):
（9）
in the formula, TinIs the indoor temperature, CairIs the equivalent heat capacity (kWh/DEG C) of air, R is the equivalent thermal resistance (DEG C/kW) of the building material,for simulation of time intervals (h), ToutIs the temperature of the outside of the room,H airheating power for air.
Establishing a PMV (predicted mean volume) index to quantify the temperature comfort, wherein the calculation formula is as follows:
（10）
when the PMV value is 0, the comfort level of the temperature is the highest, the corresponding optimal temperature is 26 ℃, the PMV range given by ISO 7730 is between-1 and 1, and therefore the indoor temperature meeting the comfort level constraint is 24.8-27.3 ℃.
The hot water load model considering comfort is as follows:
if a certain amount of hot water is output, an equal amount of cold water must be supplemented. According to the second law of thermodynamics, the relationship between the hot water temperature, the cold water volume and the amount of heat required to maintain the desired water temperature is given by the equation (11):
（11）
in the formula, TwsThe temperature of the hot water in the heat storage tank is DEG C; cwater、Vtotal、ρwThe specific heat capacity of water, the water storage capacity of the hot water tank and the density of water are respectively; t cold ws is notesThe temperature of cold water entering a hot water tank is in the range of DEG C and HwaterFor supplying heating power to the water storage tank.
The hot water load comfort constraints are as follows:
（12）
wherein Tmin ws and Tmax ws are respectively the lower limit and the upper limit of the optimal temperature of hot water, DEG C; tmin and Tmax in are respectively the lower limit and the upper limit of the indoor optimum temperature, DEG C.
And 4, step 4: establishing a master-slave game model between an operator and a user, dividing the model into an upper sub-module and a lower sub-module, solving by combining a chaotic fish swarm algorithm and a YALMIP tool box in MATLAB by taking the maximum income of the operator as an objective function at the upper layer and the minimum payment cost of the user as an objective function at the lower layer to obtain a strategy set of game parties at a balance point:
the upper-layer game model takes the highest income of an operator as an optimization target, the decision variable is the optimized electricity price of 24 periods, and the electricity purchasing amount and the natural gas purchasing amount of a superior power grid in the day are as follows:
and (3) maximizing profit:
（13）
in the formula ctThe electricity price of the time period t established for Energy Hub; cahead tElectricity prices for the day-ahead market period t; eahead tContract electric quantity for a market time t in the day ahead; gtNatural gas purchase amount for time period t; p is a radical ofgasIs the price of natural gas; ereal+ t、Ereal- tThe electric quantity bought or sold from the real-time market in the time period t respectively; g-t, g + t are eachThe electricity price is the real-time purchase and sale of the market in the time period t.
Constraint 1: electricity price restraint
Constraint 2: electric quantity balance constraint
Constraint 3: real-time market trading power constraints
The lower-layer game model takes the lowest household energy consumption cost paid by the user as an optimization target, and the decision variables are the optimized running time and indoor environment temperature T of each type of load in the optimization periodin(° c), hot water temperature TwsIn degrees centigrade. The payment function of the user in one scheduling period is as follows:
（14）
wherein,
（11）
（12）
in the formula,h tthe user needs to pay the unit heat supply cost for the operator.
Example analysis
Besides a photovoltaic power supply, the multifunctional complementary system also comprises one cogeneration type micro gas turbine and one waste heat boiler, and the application of the model in the above embodiment is realized by using MATLAB programming.
The analysis of the household user is carried out on a typical day in winter to illustrate the basic features of the method of the invention. Suppose that a grid company employs a peak-to-valley average time-of-use pricing mechanism. The natural gas price is 2.7 yuan/m3The hot water load curve and the rigidity load curve are shown in the figure(3) And FIG. 8. The flexural load parameter is shown in fig. 9. The thermal resistance of the building material, the upper and lower limits of the hot water temperature, the cold water temperature, the air thermal resistance, the specific heat capacity of water and the volume data of the water storage tank are shown in a graph (5). The research object of the invention is a residential district, and the number of the residential units is 100.
The result of the game optimization time-of-use electricity price model used in the invention after optimization is shown in figure (7). At this time, the power generation cost of the operator is 300.15, the profit is 900.53 yuan, and the payment fee of the user is 1200.68 yuan. Considering the comfort constraint of the user, such as the minimum operation time of the washing machine is 60min, the operation time is constrained to be 7-11h, and the electricity price is lowest at 11h in the period, so the operation time of the washing machine is scheduled to be 11 h.
Claims (7)
1. The method is characterized by comprising the following three steps of:
step 1: establishing a framework for building a combined heat and power household type multi-energy complementary system based on a centralized interconnected energy hub, and analyzing the operation mode of the system:
step 2: establishing a scheduling model taking the operation requirements of related equipment as constraint conditions:
and step 3: establishing a detailed scheduling operation strategy for household appliances, hot water loads and air heat loads considering user comfort;
and 4, step 4: and establishing a master-slave game model between an operator and a user to obtain a strategy set of the game parties at the balance point.
2. The method according to step 1 in claim 1, which is mainly characterized by the operation mode of a household type multifunctional complementary system:
the operator prediction center predicts the air temperature and the photovoltaic unit output in each time period of the next day in the day ahead; in the day ahead, an operator arranges the output of a gas turbine and a gas boiler according to the heat load requirement of a user in advance, preferentially meets the requirement of a photovoltaic and gas turbine set for an electric load, signs an electric power purchase contract with a superior power grid, determines the electric power purchase quantity of each time period, issues the real-time electricity price of each time period of the next day to the user, and can sell (purchase) electric energy to a real-time market when the system is in operation in real time and the electric power supply is surplus (insufficient); in the day, a Home Energy Management System (HEMS) installed inside a user family calculates the comfort level of the user according to the electricity price information issued by an operator, automatically selects the optimal power utilization strategy of various household appliances and controls the switching-on and switching-off of various translatable loads; on the other hand, on the premise of meeting the temperature comfort degree, the heat load in each time period is optimized.
3. The method of step 2 of claim 1, wherein a power model and a cost model of the micro gas turbine are created.
4. A method according to step 3 of claim 1, characterized by establishing an operation control strategy for the translatable load by converting the start time of the translatable load to be optimized into a 0,1 vector representing the start-stop status of the translatable load, taking into account the comfort constraints of the user.
5. A method according to step 3 of claim 1, characterized in that the PMV index is established to quantify the temperature comfort, and the temperature comfort is used as a constraint to model the space heat load and the hot water load.
6. The method of step 4 of claim 1, wherein an operator revenue model and a user revenue model are constructed.
7. A method according to step 4 of claim 1, characterised by solving a master-slave gaming model: dividing a master-slave game model into an upper sub-module and a lower sub-module for solving; the lower-layer sub-module refers to a user and aims to obtain a response set responding to a strategy made by an operator on the premise of ensuring the self comfort degree constraint; and solving the upper-layer game problem by adopting a chaotic artificial fish swarm algorithm, and solving the lower-layer model by using YALMIP in MTALAB to obtain a strategy knot set of an operator and a user.
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