CN115986812A - Micro-grid economic planning method and device considering energy storage and demand response - Google Patents
Micro-grid economic planning method and device considering energy storage and demand response Download PDFInfo
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Abstract
The invention provides a micro-grid economic planning method and device considering energy storage and demand response, and provides a comprehensive planning method which combines three stages of optimal power generation scale, operation planning and demand side management method to be considered simultaneously so as to realize an optimal configuration and operation planning method. The method aims to solve the problems of long-term optimal capacity and short-term operation planning of the independent micro-grid consisting of wind energy, solar power generation and ESS. The goal is to promote high penetration of renewable energy sources with the flexibility of ESS and optimal DSM control method. In addition, a variable RES matching method based on load requirements and RES power output curves is proposed, and an adaptive requirement based on an improved DSM mechanism is given.
Description
Technical Field
The invention relates to the technical field of planning and energy management such as dynamic pricing, demand response, site selection and capacity fixing and the like on a power consumer side, in particular to a micro-grid economic planning method and device considering energy storage and demand response.
Background
To meet the increasing global energy shortages and the demand for reliable, clean and affordable energy that is generally available by 2030, the united nations sustainable development goal 7 (SDG 7) mandates innovative transmutation energy development towards future sustainable development. Thus, a strong energy efficiency policy on a global scale supports large-scale integration and increased share of renewable energy sources; reducing greenhouse gas emissions from fossil fuels. Microgrid technology with renewable energy resources is becoming a major research focus worldwide, especially for islands and geographically complex places. Isolated microgrids may facilitate electrification in remote islands and rural areas where grid construction is costly or not interconnectable. The microgrid also supports large scale integration of RES, ESS, which improves the flexibility of the electric microgrid as it can operate in grid-connected or island mode when the main grid fails. However, the output of renewable energy sources is random, presenting greater challenges to the operation and control of the power microgrid. This requires the use of an expensive ESS to minimize the intermittent effects of solar and wind power output. Therefore, without proper planning, the ESS capacity is likely to be very large and may result in high costs for power microgrid planning and operation.
1. Microgrid integrated planning method considering flexibility of energy storage microgrid
Several design criteria have been set in microgrid planning and operational feasibility research literature; some of these are notable for flat electrical costs (LCOE), renewable energy proportions, load loss probability, etc. Different optimization techniques have been employed, such as robust evolutionary algorithms; the literature introduces a detailed comprehensive analysis of the intelligent microgrid design by adopting other meta-heuristic methods. Since isolated (off-grid) microgrids cannot benefit from the interconnection flexibility provided by large grids, a more specific approach is taken for the design of isolated microgrids. The management of flexibility thus depends largely on the energy storage and reliability conditions can be met by incorporating a backup generator. The controllability requirement of the renewable energy power generation is realized by meeting the requirement of properly integrating proper energy storage facilities.
Currently, various ESS technologies for microgrid flexibility management are classified according to investment cost, lifetime, power capacity, energy density, losses, defects, and the like. However, cost and capacity issues remain key challenges for effective deployment of ESS technologies. One of the emerging ESS technologies that is attracting attention is the pumped thermal storage micro grid (PTES). Compared to other existing energy storage options (such as BESS, compressed air energy storage, and pumped storage), PTES have relatively better energy density, thereby reducing unit cost per MWh capacity and installation cost per MW. Furthermore, unlike pumped storage micro-grids, the land area and terrain requirements are not complex. Therefore, the prospect of PTES in microgrid design, especially for islanding cases, is optimistic.
In the invention, the DSM method is adopted to carry out cost benefit and technical evaluation on the PTES technology compared with BESS, and the aim is to realize effective micro-grid configuration and capacity size and combine reliable micro-grid flexibility requirements.
2. Current state of research in demand side management in microgrid flexible planning
DSM is a broad class of methods that aim to improve the energy efficiency of smart grids by modifying the load demand curve. The DR program is a branch of DSM that is intended to incentivize and influence power consumers to reshape their energy needs in response to the better benefits offered by public works. The prospects of DSM and DR are enhanced by the use of modern power markets with advanced metering and regulatory liberty. With the successful deployment of DR plans, power microgrid network expansion and additional capacity planning can be postponed. This in turn reduces the daily electricity microgrid operating costs and the customer electricity charges. Various types of DR have been proposed in the literature, which are broadly classified into time-based and incentive-based DR types, as well as implementation methods and applications, according to time shifts for load demand, load reduction, or fines that do not comply with bidding contracts. The mathematical model of the DR program is intended to provide cost benefits to different stakeholders from the utility company to the distribution microgrid operator and the electricity power consumer. The time-based DR type provides power consumers with time-varying electricity rates, while the incentive-based DR type provides consumers with fixed revenue for curtailment of power consumers. The DR plan is also designed according to the power consumer type.
The current research is insufficient in that: traditional research has been focused and limited to large electric micro-grids with network interconnections, relaxed market regulations, diversified power generation sources and DR program advantages. The applicability and potential of DR planning to micro-grids remains an area of increasing interest, particularly in terms of energy costs, pollution reduction, minimization of peak load consumption, and reliability improvement. Demand side management has advantages in reducing the impact of solar and wind uncertainty on residential, commercial and industrial microgrid users. The high permeability of renewable energy based power microgrids enhances operational flexibility to accommodate the intermittency and unpredictability of power output for wind and solar power generation microgrids and capacity planning.
For high RES power generation island micro-grids, consisting mainly of WT and PV power generation, the flexibility of operation planning depends to a large extent on the support of limited and very expensive energy storage micro-grids to solve the mismatch problem between time-varying demand and power generation configuration. Therefore, the use of DSM becomes an efficient and economical microgrid control model and provides the important tool for the necessary operational flexibility management for power microgrids with high RES penetration.
Furthermore, the economic and technical aspects of microgrid design involve three key aspects, which are typically handled using stage-by-stage isolation methods in many power microgrid planning studies. These scheduling methods include optimizing capacity size, optimizing energy unit scheduling, and point-to-point energy trading. These independent planning methods often result in the overall microgrid being good or bad, such as insufficient or excessive capacity of microgrid components, high operating costs, and the like. Due to long-term planning, such goodness tends to result in models that fail to adequately capture and verify actual short-term operating conditions, which tends to become more apparent in micro-grids with high renewable energy penetration. Similarly, the short-term operation scheduling method studies assume that the optimal microgrid configuration has been predetermined, which has a significant impact on short-term operation planning in terms of operating costs, power microgrid reliability, and flexibility management requirements. This means, therefore, that the three planning models need to be unified and connected in order to achieve better technical and economic benefits for the whole power microgrid.
Disclosure of Invention
In order to solve the technical problems provided by the background art, the invention provides an economic planning method and device for a microgrid in consideration of energy storage and demand response, and provides a comprehensive planning method which combines three stages of optimal power generation scale, operation planning and demand side management method for simultaneous consideration so as to realize an optimal configuration and operation planning method. The method aims to solve the problems of long-term optimal capacity and short-term operation planning of the independent micro-grid consisting of wind energy, solar power generation and ESS. The goal is to promote high penetration of renewable energy sources with the flexibility of ESS and optimal DSM control method. In addition, a variable RES matching method based on load requirements and RES power output curves is proposed, and an adaptive requirement based on an improved DSM mechanism is given.
In order to achieve the purpose, the invention adopts the following technical scheme:
a microgrid economic planning method considering energy storage and demand response comprises the following steps:
the method comprises the following steps: considering the combined application of a wind driven generator, a photovoltaic microgrid, a diesel generator and battery energy storage, establishing an economic comprehensive planning model of the island microgrid containing high renewable energy, and providing an operation capacity planning method corresponding to different operation working conditions;
step two: establishing an economic control model based on dynamic pricing by combining available capacity of instantaneous renewable energy sources based on a microgrid economic comprehensive planning model to obtain a real-time demand response method;
step three: and combining the model in the first step with the model in the second step, giving an optimization solving method taking the minimum running cost as an objective function, wherein the objective function comprises equivalent annual investment cost, running cost and cost based on a demand side management method, and specifically giving constraint conditions, and solving by using a mixed integer linear programming algorithm solving method based on MATLAB software to obtain an energy storage-renewable energy-water pumping and energy storage combined control instruction.
Further, in the first step, the method for establishing the model and planning the operation capacity corresponding to different operation conditions includes the following steps:
step 101, establishing an island microgrid economic comprehensive planning model containing high renewable energy sources comprises the following steps: establishing a power grid micro-grid model, wherein the model comprises model parameters, set economic parameters and normalized data, and initializing an optimization process;
102, solving by using MILP to obtain the maximum renewable energy permeability and the optimal capacity of energy storage and water pumping energy storage, wherein the flexibility of the method comprises two parts: energy storage and demand side response capacity;
103, judging whether the power consumer demand is greater than the clean energy power supply capacity, if the renewable energy output is greater than the power consumer demand, judging whether the energy storage micro-grid is less than the maximum energy storage capacity, and if the renewable energy output is greater than the power consumer demand, storing energy and charging so as to increase the surplus electric quantity; if the energy storage micro-grid is more than the maximum energy storage capacity, starting a demand response method to increase the demand of the power consumer;
and 104, judging whether the power user requirement is smaller than the clean energy power supply capacity, if the renewable energy output is smaller than the power user requirement, judging whether the energy storage micro-grid is more than the minimum energy storage capacity, and if the surplus capacity exists, storing energy and discharging so as to meet the power user requirement. If the energy storage micro-grid is less than the minimum energy storage capacity, namely the energy storage capacity is not enough to meet the requirement of the power consumer, starting a demand response method to reduce the requirement of the power consumer, and otherwise, returning to recalculate the optimal capacity; and if the micro-grid still does not reach the balance of the power generation power after the demand response, starting the diesel generator, meeting the demand of the power consumer, and detecting whether unbalanced power exists or not.
And 105, calculating the grid-connected capacity of each part to obtain a capacity size decision variable, and setting an optimal demand response action and a planning load demand curve, namely an optimal DSM method, based on the demand response quantity of the transferable power users.
Further, the second step of the economic control model-RGDP based on dynamic pricing comprises the following steps:
RGDP is a time-based, improved DR method that facilitates minimization of power generation costs and maximization of benefits to power consumers and utilities. In the method, the power company provides flexible pricing according to the difference between the renewable energy output and the demand, and promotes power users to actively shift power utilization plans according to the provided prices.
1) The method comprises the following steps of (1) an equality constraint condition that the net power consumer of a microgrid containing a high-proportion renewable energy island is 0, namely the total energy demand amount of the power consumer before and after the demand response is implemented should be equal:
2) Considering the demand response electricity price interval constraint of the electricity price, the new electricity price at any time t
All within the highest and lowest limits:
3) The control logic is as follows: the novel electricity price model is controlled as a function of the mismatch between the variable RES power generation per time period and the electricity consumer demand, where the control objective is expressed as:
4) The novel electricity price model is based on renewable energy generating capacity capable of providing the lowest generating cost, and an optimal power consumer demand curve is calculated by using demand response, so that the lowest power cost is brought to a user, and an RGDP power consumer demand model is defined as follows:
in the above formula:
t is the total time period (hours) within the scheduling range,
segment and P L (t) representing dynamic pricing method for power generation of renewable energy sources respectivelyAfter normal and before control, an electrical energy requirement>
Is the new electricity price at the moment t>
And &>
Minimum and maximum values of electricity prices, P pv : instantaneous output power (kW), P of photovoltaic system wt : instantaneous output power (kW) of the wind turbine system>
Is a standard electricity price, phi e(i,j) For the phase price sensitivities of the ith and jth time periods, ad (i) and Ds (i) provide the incentives and penalties for the ith and jth time periods, respectively.
The invention also provides a device for realizing the microgrid economic planning method considering energy storage and demand response, which comprises a processor and a memory;
wherein the processor is configured to execute the method for economic planning of the microgrid taking into account energy storage and demand response;
the memory is used for storing executable instructions of the processor.
The invention also provides a computer storage medium, on which a computer program is stored, the computer program being executed by a processor, and the method for economic planning of a microgrid taking energy storage and demand response into account is implemented.
Compared with the prior art, the invention has the beneficial effects that:
1) The method provides a comprehensive optimization framework for the first time, wherein the comprehensive optimization framework comprises the capacity of each component object of the energy microgrid, an operation method and DSM response, and is used for designing the high RES microgrid with enough operation flexibility.
2) The method firstly establishes a dynamic pricing demand Response (RGDP) DR method based on renewable energy power generation, takes time change of renewable energy into consideration, and is used as a tool for effective microgrid planning and demand side flexibility enhancement.
Drawings
Fig. 1 is a flow diagram of a microgrid integrated planning framework of the present invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
Example one
A microgrid economic planning method considering energy storage and demand response comprises the following steps:
step 1: considering the combined application of a wind driven generator, a photovoltaic microgrid, a diesel generator and battery energy storage, establishing an economic comprehensive planning model of the island microgrid containing high renewable energy, and providing an operation capacity planning method corresponding to different operation working conditions;
step 2: establishing an economic control model based on dynamic pricing by combining the available capacity of instantaneous renewable energy sources based on a microgrid economic comprehensive planning model to obtain a real-time demand response method;
and step 3: and combining the model in the first step with the model in the second step, giving an optimization solving method taking the minimum running cost as an objective function, wherein the objective function comprises equivalent annual investment cost, running cost and cost based on a demand side management method, and specifically giving constraint conditions, and solving by using a mixed integer linear programming algorithm solving method based on MATLAB software to obtain an energy storage-renewable energy-water pumping and energy storage combined control instruction.
Specifically, the following contents are included:
step 1, considering a demand response method, and based on renewable energy micro-grid comprehensive planning model framework
Fig. 1 is a flow diagram of a joint planning framework for optimizing capacity size and operation planning, with or without consideration of DR planning in a microgrid. The diesel generator set is used as a standby power supply during the period of insufficient power supply of the RES and the ESS; after the DSM. The model also takes into account two types of load requirements that are available for DSM implementationMovement (Δ P) L ) And immovable loads. P is RES Representing total RES and DPSP representing a power supply deficit.
The comprehensive planning method has the following ideas:
firstly, establishing a power grid micro-grid model, wherein the model comprises model parameters, set economic parameters, normalized data and the like, and initializing an optimization process;
and secondly, realizing the maximum renewable energy penetration fraction and the optimal capacity size of operation optimization by using MILP, and adding a judgment logic after obtaining an optimal value. The flexibility of the proposed method is mainly composed of two parts: the system comprises an energy storage microgrid and a demand side response capacity.
And thirdly, judging whether the power consumer demand is greater than the clean energy power supply capacity, if the renewable energy output is greater than the power consumer demand, judging whether the energy storage micro-grid is less than the maximum energy storage capacity, and if the renewable energy output is greater than the power consumer demand, storing energy and charging so as to increase the surplus electric quantity. And if the energy storage micro-grid is more than the maximum energy storage capacity, starting a demand response method, and increasing the demand of the power consumer.
And judging whether the power user demand is smaller than the clean energy power supply capacity or not, if the renewable energy output is smaller than the power user demand, judging whether the energy storage microgrid is more than the minimum energy storage capacity or not, and if the renewable energy output is larger than the minimum energy storage capacity, storing energy and discharging so as to meet the power user demand. If the energy storage micro-grid is less than the minimum energy storage capacity, namely the energy storage capacity is not enough to meet the requirements of the power users, starting a demand response method to reduce the requirements of the power users, and otherwise, returning to recalculate the optimal capacity; and if the micro-grid still does not reach the balance of the power generation power after the demand response, starting the diesel generator, meeting the demand of the power consumer, and detecting whether unbalanced power exists or not.
And finally, obtaining capacity size decision variables (calculating grid-connected capacity of each part), setting operation planning instruction actions, setting optimal demand response actions and planning load demand baselines (an optimal DSM method) based on transferable power user demand response quantity.
Step 2, demand response dispatching and economic model
User's ability to change consumption patternsReceiving an influence of economic factors such as offered price incentive, incentives and penalties; these factors depend on the type of DR employed. The DR model takes into account two types of load requirements, namely elastic and inelastic load requirements. The spring load requirements are flexible in time usage, i.e. the usage time can be adjusted from one period to another, such as dishwashers and water pumps. Inelastic loading is a non-delayed requirement with a fixed power usage time. Inelastic loads can be further divided into two categories, i.e., adjustable loads such as heating, ventilation and air conditioning (HVAC) units and non-adjustable loads, including lighting loads that can only be turned on or off. Change in electricity price at ith or jth time period, respectively: (
Or->
) Will result in a power user demand (@ h) for the jth or ith period>
Or->
) The number of (c) varies. Therefore, the power consumer demand for all time periods (T) is affected by the change in electricity prices for each particular ith or jth time period.
Price flexibility of load demand (phi) e(i,i) ) The behavioral pattern of electricity demand is defined as the price change specified by the DR pricing scheme:
the multi-epoch price sensitivity for the ith epoch relative to the jth epoch is given by:
time demand response model
TOU DR is a time-based demand response program that provides a fixed pricing scheme for different periods of the cycle time range under consideration, such as a day or week. The TOU rates typically provide a predetermined price based on microgrid load, such as a peak rate during peak load demand periods (also referred to as peak hours). The electricity fee during this period will be charged at a peak electricity rate (peak electricity rate) and vice versa, during low power consumer periods (off-peak periods), a possibly lower electricity rate (off-peak period electricity rate) is performed. The final TOU response load economic model is given by:
wherein
Is the electricity price enforced when the TOU DR program is selected as the DSM method, and Ad and Ds are the incentives and penalties provided in the ith and jth epochs, respectively.
Direct power consumer control demand response model
DLC is a classic incentive-based DR program that provides incentives to power consumers in the form of payments or electricity credit as they reduce their demand; or when the utility remotely cycles or regulates the customer's electrical equipment consumption length during an emergency or peak load demand. The contract power consumer control technology of DLC programs has been adopted globally, primarily to reduce power consumers of residential and commercial power consumers during peak power consumer demand. The final responsive DLC load demand model is defined as:
wherein
Is to selectDLC DR plans electricity prices to be enforced as a DSM method.
Isolated microgrid dynamic pricing demand response model based on renewable energy power generation
The RGDP provided by the method is an improved time-based DR program, and is specially customized for an isolated microgrid lacking liberalization market relaxation. Minimizing the cost of electricity generation translates into maximizing the interests of the customer and the utility company. Thus, in the present approach, the utility provides flexible pricing based on the difference between renewable energy output and demand, and the power consumer is motivated to shift load based on the provided price.
The following are features of the RGDP model:
the total energy demand of the power consumers before and after the demand response is implemented should be equal; indicating that no load shedding should occur. The proportion of flexible demand resources considered is completely resilient.
New electricity price at any time t
Both at the highest and lowest level.
The new RGDP DR electricity price model is implemented as a function of the mismatch between the variable RES power generation per time period and the customer electricity consumer demand, expressed as:
the RGDP model applies appropriate demand response actions to determine an optimal power consumer demand curve/profile based on the renewable energy generation amount that provides the lowest generation cost; thereby bringing the lowest cost of electricity to the user. Therefore, the RGDP end power user demand model is defined as:
step 3, problem formulation of mixed integer linear programming
The optimization problem solved in the present method is expressed as an MILP problem based on the mathematical model of the microgrid used. The MILP algorithm has been used to solve the problem of comprehensive optimization planning and operation of the electric microgrid for both grid-connected and independent microgrids. The MILP algorithm is robust and easy to process, and is used to simulate a microgrid consisting of hybrid RES generators. Thus, the method can be adopted
MILP in the environment to solve the optimization problem. The optimization problem is expressed as a mixed integer linear equation set consisting of two types of variables x; real and binary variables. The formula for the MILP problem is shown below:
f (x) is an objective function expressed as a vector consisting of linear combinations of decision variables. The matrices Aeq, A and their corresponding vectors beq, b are the constraints modeled as inequality and equality, respectively; lb and ub are the lower and upper limits of the decision variables. x is the column vector of the decision variables. The present study used the MATLAB intinprog optimization toolkit to run the optimization program.
Objective function
The method mainly aims to fully utilize the function of DSM and reduce the total cost of the microgrid to the maximum extent, including investment, replacement, maintenance and operation cost in the aspects of long-term capacity adjustment and short-term operation planning. Therefore, the aggregated objective function of the microgrid design integration framework is defined as follows:
Minimize:Total microgrid costs ＝f eac +f run +f dsm (10)
equivalent annual cost of long term capacity adjustment
Objective function f eac (6) The first part of (a) is to minimize the total Equivalent Annual Cost (EAC), including capital costs, replacement costs (applicable only to BESS and diesel generators), operating and maintenance costs over the life cycle of the project, as shown by (7) below.
The discount rate is used to convert all costs to their present values.
Wherein Y is the life cycle of the product, r is the discount rate, C k Indicating the total cost of the subsidence of each microgrid component, and the subscript k indicates the microgrid components, i.e., PV, WT, BESS, PTES, and diesel generators. C k Representing the cost of each microgrid component. C k And the detailed cost components of the decision variables are detailed below:
a.C pv : photovoltaic microgrid cost;
wherein CI pv Initial investment cost, OMC pv Is the present value of the cost of operation and maintenance,
is a decision variable representing the capacity of the photovoltaic microgrid.
b.C wt : wind generator cost;
wherein CI wt Initial investment cost, OMC wt Is the present value of the cost of operation and maintenance,
watch with clockAnd (5) showing a decision variable of the photovoltaic microgrid capacity.
c.C ptes : cost of PTES;
wherein CI ptes Initial investment cost, OMC ptes Is the present value of the cost of operation and maintenance,
is a decision variable representing the capacity of the PTES microgrid.
d.C bess : the cost of BESS;
wherein CI bess Initial investment cost, OMC bess And OMC bess Are the present values of replacement costs, operating costs and maintenance costs,
is a decision variable representing the capacity of the BESS microgrid.
e.C diesel : the cost of diesel oil;
C diesel ＝(MC diesel ×H run +RC diesel ) (16)
wherein RC diesel And MC diesel Present values of replacement and maintenance costs, H run Representing the number of hours the diesel generator is operating. The diesel generator is replaced after every 20,000 hours of use.
Hourly operation cost based on unit scheduling
Objective function f run The second component of (a) is the generator set-based microgrid operating/operating cost. The operating (fuel) cost is proportional to the output power produced per hour per energy conversion technology; and it directly affects the optimization of diesel generators in different scenarios with different REPFAnd (6) scheduling.
Wherein F diesel (P g (t)) is a fuel cost function for an existing diesel generator expressed as:
wherein c is f As fuel cost (US $/L), P g 、
a. b is output power (kW), rated capacity (kW), fuel curve intercept coefficient (L/h/kW) and fuel curve slope (L/h/kW), respectively.
Cost based on demand side management method
Objective function f dsm The third component of (a) is the operational costs associated with controlled/flexible load demand rescheduling or curtailment based on the chosen DSM approach. The DSM-based operating cost is given by:
wherein P is c Is a reduction of power consumer demand based on the selected DSM approach, A d Is the incentive cost value in $/kWh. The microgrid operator compensates the end power consumer for the reduction in power consumer demand through an incentive payment.
Microgrid constraint condition
1) And power balance constraint: the total power produced by the WT, PV, diesel generator and ESS should always meet the load requirements of all considered scenarios.
Wherein
And &>
Is coming from>
Or->
Depending on the scenario.
2) Energy storage microgrid constraint: state of charge limitation of ESS: the state of charge of the ESS must be within the maximum and minimum boundary condition limits of the ESS.
SOC min ≤SOC(t)≤SOC max (21)
3) Output power constraint of the diesel generator: power output (P) of diesel generator g ) Subject to the upper limit of power generation
And lower limit of
The limit of (2).
4) Flexible load demand capacity constraints: flexible load capacity Δ P L Is limited to the maximum
And minimum->
Allowing flexible shifting within the load demand capacity (within 10% of the initial load demand).
5) And (4) limiting the new electricity price: new electricity prices at any time after DR planning are at maximum
And a minimum price limit->
And (4) inside.
The variables in the above formula are explained together as follows:
A wt : wind area (m) of wind wheel blade 2 )。
I stc : intensity of solar radiation (1 kW/m) under standard test conditions 2 )。α p : temperature coefficient of photovoltaic module.
ΔP L : flexible load demand (kW).
ρ: air Density (kg/m) 2 )。
BESS: battery energy storage microgrid.
C P : wind turbine power coefficient.
DEPF: diesel energy penetration fraction.
And Dg: argon gas Density (kg/m) 3 )。
DLCDR: directly controlling the load demand response.
DR: and (5) demand response.
A DSM; and (4) demand side management.
ESS: energy storage microgrid.
M g : argon gas mass (kg).
M r : and (5) the mass (kg) of the pumped storage microgrid medium.
P g : instantaneous output power (kW) of the diesel generator.
P L : load demand (kW).
P pv : instantaneous output power (kW) of the photovoltaic microgrid.
P RES : total renewable energy instantaneous output power (kW).
PV: photovoltaic power generation microgrid.
r: discount rate (%).
REPF: permeability of renewable energy.
RES: a renewable energy source.
RGDPR: renewable energy based dynamic pricing demand response. SH (hydrogen sulfide) g : specific thermal Density of argon (J kg) -1 K -1 )。
SH r : specific heat density (J kg) of storage medium -1 K -1 )。
SOC max : maximum energy storage microgrid charges (kWh).
SOC min : minimum energy storage microgrid charges (kWh).
T: the total time period (hours) within the range is scheduled.
T pv : photovoltaic module temperature.
TOUDR: time demand response is used.
And (Uef): argon volumetric efficiency (%).
v ci : cut-in wind speed.
V co : and cutting out wind speed.
Vs: argon volumetric flow rate.
WT: a wind power generator.
Y: equipment life (year).
Y pv : photovoltaic microgrid loss factor (%).
I I : incident solar radiation (W)/m 2 )。
The method has the following advantages:
1. the handling of RES uncertainty is crucial to the mismatch problem between the power generation and power consumer demand curves, as there is no schedulability or controllability of the power generation source. Therefore, this requires incorporating an ESS into energy management, balancing fluctuating load demands and smoothing out the stochastic output of renewable energy power generation. In the present method, the PTES are charged and discharged during periods of excess and insufficient power generation, respectively, to counteract the mismatch between the power generation and power consumer demand curves.
2. In a high renewable energy based microgrid configuration, the energy storage capacity contributes to cost, the method adopts a PTES-based microgrid configuration to save the total cost significantly, and the cost benefit of adopting a PTES-based microgrid instead of a traditional BESS-based microgrid is attributed to the potential of PTES to achieve a higher depth of discharge compared to BESS.
3. The resulting economic TOU load demand profile reduces the capacity of the WT and PTES as the power consumer accepts the TOU-varying power charges for each period, thereby creating a microgrid configuration that provides a common cost benefit to the grid company and the power consumer. Power consumers receive revenue through reduced power expenditure, while power companies realize reduced total microgrid cost. Microgrid efficiency is significantly increased as power consumers adjust load demands to achieve minimum power costs. The increase in microgrid efficiency is a result of the shifting of load demand from peak rates to off-peak rate pricing periods. Accordingly, the power generation cost of the electric power company and the bill of the electric power customer are reduced.
4. The RGDPDR method proposed by the present method minimizes the difference between the available renewable energy generation and the load demand. Therefore, a dynamic pricing scheme based on the mismatch between the power consumer demand and the power generation distribution is employed. The use of RGPDR has significant benefits for various aspects of the planning framework. From a demand perspective, power consumers enjoy the highest cost savings when consuming the same amount of energy as compared to the reference case without the DR plan. Furthermore, RGDPDR provides the cheapest total electricity charge to the power consumer compared to the DLC and TOU DR plans. Furthermore, the total energy consumed is equal to the initial energy demand. It is stated that DR does not interfere with customer load requirements and their requirements are always fully satisfied, which makes it superior to DLC and TOUDR. Demand side cost savings result from the power consumer shifting the power consumer from high price periods to low price periods.
5. From a power generation perspective, implementation of the RGDPDR plan reduces WT and PTES capacity compared to no DR plan. The price can be significantly reduced compared to DLC and TOUDR types. Thus, the overall cost of each object's EAC and microgrid is reduced, and the PTES capacity of the new microgrid configuration is reduced. Furthermore, while the microgrid reduces the capacity of the WTs, the capacity of the PVs may be increased. This is due to the fact that the EAC of the PV is relatively inexpensive compared to the WT.
In summary, the innovativeness of the RGDP DR method is achieved by providing flexible, dynamic pricing as a function of the difference between generated power and load demand; due to new pricing schemes, electricity consumers are motivated to shift their electricity consumer demand from high electricity price periods to low electricity price periods; thereby generating a new optimal load curve. The new optimal RGDP load curve is much cheaper to obtain in terms of microgrid planning costs for the utility company, thus reducing the electricity costs for the end power consumers. The RGDPDR method proposed by the present method tends to push peak power consumer demand to match the peak period of the renewable energy generation curve. By minimizing the difference between the instantaneous power production and the corresponding load demand, the capacity of the ESS (PTES) is reduced.
In conclusion, the invention provides an island micro-grid comprehensive optimization planning method considering the cost of the energy storage Battery (BESS) and the demand response. The method combines the renewable energy long-term capacity scale planning, operation optimization and demand side flexibility management methods to improve the technical economy of the energy microgrid; the optimal demand side management method improves the short-term flexibility of the micro-grid by reducing the mismatching between power generation and power user curves to the maximum extent, thereby realizing the minimization of the cost of the power grid and the cost of the power, and promoting the gradual transition of the Renewable Energy Permeability (REPF) of the current power grid to 100 percent based on green energy. Therefore, firstly, the method provides a renewable energy source-demand response method-diesel generator cooperative regulation and control method, and improves the economic microgrid configuration efficiency of the microgrid technology. Secondly, compared with the traditional method only depending on BESS, the method improves the operation economy of the power grid because of considering the flexible coordination of pumped storage (PTES) and batteries; thirdly, a capacity optimization method based on renewable power generation-demand response dynamic pricing (RGDP-DR) is formulated, so that the net difference between the RES power generation amount and the load demand is minimum, and the total cost of the microgrid is further reduced. Therefore, the method can provide an important theoretical basis for optimizing and scheduling the micro-grid containing the high-permeability renewable energy.
Example two
The embodiment of the invention also provides a device for realizing the microgrid economic planning method considering energy storage and demand response, which comprises a processor and a memory;
wherein the processor is configured to execute the method for economic planning of the microgrid taking into account energy storage and demand response;
the memory is used for storing executable instructions of the processor.
EXAMPLE III
As a third embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the method for economic planning of a microgrid taking into account energy storage and demand response.
As will be appreciated by one skilled in the art, 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 so forth) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (5)
1. A method for economic planning of a microgrid in consideration of energy storage and demand response, comprising the steps of:
the method comprises the following steps: considering the combined application of a wind driven generator, a photovoltaic microgrid, a diesel generator and battery energy storage, establishing an economic comprehensive planning model of the island microgrid containing high renewable energy, and providing an operation capacity planning method corresponding to different operation working conditions;
step two: establishing an economic control model based on dynamic pricing by combining the available capacity of instantaneous renewable energy sources based on a microgrid economic comprehensive planning model to obtain a real-time demand response method;
step three: and combining the model in the step one with the model in the step two, giving an optimization solving method taking the minimum operation cost as an objective function, wherein the objective function comprises equivalent annual investment cost, operation cost and cost based on a demand side management method, specifically giving constraint conditions, and solving by using a mixed integer linear programming algorithm solving method based on MATLAB software to obtain an energy storage-renewable energy-water pumping and energy storage combined control instruction.
2. The economic planning method for a microgrid considering energy storage and demand response according to claim 1, characterized in that in the first step, the establishment of the model and the planning method for the operation capacity corresponding to different operation conditions comprise the following steps:
step 101, establishing an island microgrid economic comprehensive planning model containing high renewable energy sources comprises the following steps: establishing a power grid micro-grid model, wherein the model comprises model parameters, set economic parameters and normalized data, and initializing an optimization process;
102, solving by using MILP to obtain the maximum renewable energy permeability and the optimal capacity of energy storage and water pumping energy storage, wherein the flexibility of the method comprises two parts: energy storage and demand side response capacity;
103, judging whether the power consumer demand is greater than the clean energy power supply capacity, if the renewable energy output is greater than the power consumer demand, judging whether the energy storage micro-grid is less than the maximum energy storage capacity, and if the renewable energy output is greater than the power consumer demand, storing energy and charging so as to increase the surplus electric quantity; if the energy storage micro-grid is more than the maximum energy storage capacity, starting a demand response method to increase the demand of the power consumer;
and 104, judging whether the power user requirement is smaller than the clean energy power supply capacity, if the renewable energy output is smaller than the power user requirement, judging whether the energy storage microgrid is more than the minimum energy storage capacity, and if the renewable energy output is larger than the power user requirement, storing energy and discharging so as to meet the power user requirement. If the energy storage micro-grid is less than the minimum energy storage capacity, namely the energy storage capacity is not enough to meet the requirements of the power users, starting a demand response method to reduce the requirements of the power users, and otherwise, returning to recalculate the optimal capacity; and if the micro-grid still does not reach the balance of the power generation power after the demand response, starting the diesel generator, meeting the demand of the power consumer, and detecting whether unbalanced power exists or not.
And 105, calculating the grid-connected capacity of each part to obtain a capacity size decision variable, and setting an optimal demand response action and a planning load demand curve, namely an optimal DSM method, based on the demand response quantity of the transferable power users.
3. The method for economic planning of a microgrid considering energy storage and demand response as claimed in claim 1, characterized in that the economic control model based on dynamic pricing in the second step comprises the following steps:
1) The method comprises the following steps of (1) equality constraint condition that the net power consumer of a microgrid containing a high-proportion renewable energy island is 0, namely the total energy demand of the power consumers before and after the demand response is implemented is equal:
2) Response to a demand for electricity price interval constraint, new at any time t, taking into account electricity pricePrice of electricity
Both at the highest and lowest limit:
3) The control logic is as follows: the novel electricity price model is controlled as a function of the mismatch between the variable RES power generation and the electricity consumer demand per time period, wherein the control objective is expressed as:
4) The novel electricity price model is based on renewable energy generating capacity capable of providing the lowest generating cost, and an optimal power consumer demand curve is calculated by using demand response, so that the lowest power cost is brought to a user, and an RGDP power consumer demand model is defined as follows:
in the above formula:
t is the total time period in the scheduling range, in hours,
segment and P L (t) represents the electric energy demand after the renewable energy power generation dynamic pricing method and before the control respectively, and is based on the value of the electric energy demand>
Is the new electricity price at the moment t>
And &>
Minimum and maximum values of electricity prices, P pv : instantaneous output power of photovoltaic system in kW, P wt : instantaneous output power of the wind driven generator system, in kW @>
Is a standard electricity price, phi e(i,j) For the phase price sensitivities of the ith and jth periods, ad (i) and Ds (i) provide incentives and penalties for the ith and jth periods, respectively.
4. An apparatus for implementing a microgrid economic planning method taking energy storage and demand response into account as claimed in any one of claims 1 to 3, characterized by comprising a processor and a memory;
wherein the processor is configured to execute the method of economic planning of a microgrid in consideration of energy storage and demand response as claimed in any one of claims 1 to 3;
the memory is used for storing executable instructions of the processor.
5. A computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement a method for economic planning of a microgrid taking into account energy storage and demand response as claimed in any one of claims 1 to 3.
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| CN117277444B (en) * | 2023-11-17 | 2024-03-19 | 中国电力科学研究院有限公司 | New energy base power capacity optimal configuration method and device |
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