An analysis method and system for power grid demand side load balancing

Abstract

The application discloses a kind of power grid demand side load balancing analysis method and system in the field of microgrid dynamic pricing, method includes: current storage total electric quantity and current load demand condition in microgrid are collected, and the power supply and demand of microgrid is analyzed in combination with energy storage model, and microgrid power supply model is constructed;With the maximum benefit of microgrid and distribution network as the purpose to construct the objective function of microgrid power supply model;The objective function is solved using Markov decision model to obtain pricing strategy;Pricing strategy is optimized using Q learning algorithm, and microgrid time-of-use pricing strategy is obtained;The electricity traded between microgrid and distribution network is priced by microgrid time-of-use pricing strategy;The present application takes into account the interests of many parties, establishes a reasonable pricing model and market transaction mechanism, and balances the supply side and demand side load through dynamic price adjustment, while promoting the development of microgrid, ensures the healthy and stable development of power market.

Description

Technical Field

[0001] This invention belongs to the field of dynamic pricing for microgrids, specifically relating to an analysis method and system for load balancing on the demand side of a power grid. Background Technology

[0002] A microgrid is a miniature power grid composed of distributed generation systems, energy storage systems, and loads. It is an autonomous system capable of self-control, protection, and management. During the operation of a microgrid, the power supply comes from the conversion of renewable energy sources such as wind and solar power in the microgrid's location. However, due to the uneven and highly random nature of this energy output, and limitations imposed by the microgrid's scale and battery capacity, it is impossible to meet load demand at all times.

[0003] With the deepening of electricity market reform and the development of microgrids, a large number of microgrids of varying sizes will be connected to the distribution network in the future power system. There are both related and conflicting interests between microgrids and the distribution network. The demand for electricity by the distribution network is divided into peak and off-peak periods; how to balance the interests of multiple parties and regulate the imbalance between supply and demand has become a pressing technical problem that the electricity trading market needs to solve. Summary of the Invention

[0004] The purpose of this invention is to provide an analysis method and system for load balancing on the demand side of a power grid, taking into account the interests of multiple parties, establishing a reasonable pricing model and market trading mechanism, balancing the load on the supply and demand sides, and ensuring the healthy and stable development of the electricity market while promoting the development of microgrids.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides a method for analyzing the demand-side load balance of a power grid, comprising:

[0007] The operating parameters and environmental monitoring data of the power generation system in the microgrid are obtained, and the power generation of the power generation system is calculated; an energy storage model of the microgrid is established based on the power generation of the power generation system.

[0008] The total stored electricity and current load demand in the microgrid are collected, and the power supply and demand of the microgrid are analyzed in combination with the energy storage model to construct a microgrid power supply model.

[0009] The objective function of the microgrid power supply model is constructed with the aim of maximizing the benefits of microgrids and distribution networks; the pricing strategy is obtained by solving the objective function using a Markov decision model.

[0010] Using the expected reward of maximizing total benefit within a set period, the Q-learning algorithm is used to optimize the pricing strategy to obtain a microgrid time-of-use pricing strategy; the microgrid time-of-use pricing strategy is used to price the electricity traded between the microgrid and the distribution network.

[0011] Preferably, the method for obtaining the operating parameters and environmental monitoring data of the power generation system in the microgrid, and calculating the power generation capacity of the power generation system includes:

[0012] The solar radiation intensity, temperature, air density, and wind speed in the environment where the microgrid is located are collected as environmental monitoring data; the area of ​​photovoltaic panels, the conversion efficiency of photovoltaic panels, the swept area of ​​wind turbine blades, and the conversion efficiency of wind farms in the power generation system are collected as operating parameters.

[0013] The formula for calculating the power generation of a photovoltaic generator set is as follows:

[0014]

[0015] In the formula, η represents the photovoltaic power generation of the photovoltaic generator in microgrid c during time period t; I represents the solar radiation intensity; S represents the area of ​​the photovoltaic panel; η represents the photovoltaic power generation of the photovoltaic generator in microgrid c during time period t. PV The conversion efficiency of the photovoltaic panel; α PV T is the temperature coefficient; PV The ambient temperature for photovoltaic power generation; T cref This is a reference value for the ambient temperature of photovoltaic power generation.

[0016] The formula for calculating the power generation of a wind turbine generator is as follows:

[0017]

[0018] In the formula, Let C be the wind power generation of the wind turbine generators in microgrid c during time period t; p Power coefficient; A Wind ρ is the swept area of ​​the blades; air η is the air density; v is the wind speed at which the wind turbine in the wind turbine generator is subjected to force; η is the wind speed at which the wind turbine is subjected to force. Wind This refers to the wind farm conversion efficiency of the wind turbine generator set.

[0019] Preferably, the energy storage model of the microgrid is established based on the power generation capacity of the power generation system, expressed by the following formula:

[0020]

[0021] In the formula, Q t,c Let be the energy storage capacity of the energy storage device in microgrid c during time period t; Δt is the time period step. Let be the wind power generation of the wind turbine generators in microgrid c during time period t; Let be the photovoltaic power generation of the photovoltaic generator in microgrid c during time period t.

[0022] Preferably, the formula for constructing the microgrid power supply model is as follows:

[0023]

[0024]

[0025]

[0026] In the formula, This represents the actual discharge amount of the energy storage device in the microgrid during time period t. Q represents the actual discharge of the energy storage device in the microgrid during time period n; n,c This represents the amount of electricity stored in the energy storage device in microgrid c during time period n; This represents the theoretical discharge amount of the energy storage device in the microgrid during time period t. The total electrical charge of the energy storage devices in the microgrid before discharge in time period t; ed t,c The load demand of the microgrid during time period t; ed n,c This represents the load demand of the microgrid during time period n; δ t A represents the discharge efficiency of energy storage devices in a microgrid. max A represents the energy threshold for the initial power supply of the energy storage device. min This represents the energy threshold at which the energy storage device terminates its power supply.

[0027] Preferably, the objective function for constructing the microgrid power supply model with the aim of maximizing the benefits of the microgrid and distribution network is calculated as follows:

[0028] max B = β·max B RT +(1-β)·max B MG

[0029] In the formula, max B represents the maximum total benefit of the system; max B MG For the maximum total benefit of the microgrid; max B RT The maximum total benefit for the electricity sales company; β is the transaction weighting coefficient;

[0030] The maximum total benefit of the microgrid is max B. MG The formula for expressing it is:

[0031]

[0032] C MG =ec t,c μ t,c

[0033]

[0034]

[0035]

[0036] In the formula, C MG Electricity purchase cost for microgrids; P represents the satisfaction score of the microgrid during time period t; O For generator set operation and maintenance costs; er t,c α represents the load consumption during time period t. c The satisfaction coefficient; This represents the operation and maintenance cost coefficient; μ t,c The sales price of electricity in the distribution network during time period t; ec t,c This represents the actual load consumed after demand response.

[0037] The maximum total benefit of the electricity sales company is max B. RT The formula for expressing it is:

[0038]

[0039] C RT =ec t,c ·(μ t,c -ν t )

[0040]

[0041] In the formula, C RT For the electricity sales profit of the distribution network; χ represents the high load penalty; χ is the high load penalty coefficient.

[0042] Preferably, methods for obtaining pricing strategies by solving the objective function using Markov decision models include:

[0043] The microgrid load demand during time period t will be used as the state s of the Markov decision model. t The electricity price will be set by the distribution network during time period t as the action a of the Markov decision model. t Construct the reward r(s) for utility in energy trading within time period t. t ,a t The formula is:

[0044]

[0045] Based on the reward r(s) t ,a t Calculate the current value R of future rewards.t The formula is as follows:

[0046] R t =r(s t+1 ,a t+1 )+γ·r(s t+2 ,a t+2 )+γ 2 ·r(s t+3 ,a t+3 )+......+γ T-t-1 ·r(s T ,a T )

[0047] In the formula, γ represents the discount factor; r(s) t+1 a t+1 ), r(s) t+2 a t+2 ), r(s) t+3 a t+3 ) and r(s T a T ) represent the utility rewards in energy transactions within the (t+1), (t+2), (t+3), and (T) periods, respectively;

[0048] Based on the current value R of future rewards t Establish a pricing strategy.

[0049] Preferably, the method for optimizing the pricing strategy using the Q-learning algorithm, with the expected reward being the maximum total benefit obtained within a set period, to obtain the time-of-use pricing strategy for microgrids includes:

[0050] The expected reward E(R) is the total benefit obtained within a set period. t The formula is:

[0051] Q k (s t ,a t )=E(R t )

[0052] Q k (s t a t ) represents the state s t Next, execute action a t Q value;

[0053] The distribution network selects action a based on the Q value and a greedy strategy. t The Q value is iteratively updated, expressed by the following formula:

[0054] Q k+1 (s t ,a t )←Q k(s t ,a t )+θ·[R t +γ·max Q k (s t+1 ,a t )-Q k (s t ,a t )]

[0055] In the formula, θ is the learning rate, i.e., the speed at which the Q-value is learned; state s t+1 This is expressed as the microgrid load demand during the t+1 time period;

[0056] When the Q value gradually converges to the maximum value, the iteration ends and the optimal microgrid time-of-use pricing strategy at the current moment is output.

[0057] Q*(s t ,a t )=max Q(s t ,a t )

[0058] μ*=arg max Q*(s t ,a t )

[0059] In the formula, μ * This represents the optimal time-of-use pricing strategy for the microgrid at the current moment.

[0060] Preferably, this also includes setting upper and lower limits for the electricity sales price of the distribution network.

[0061] Preferably, the method further includes, after the set period ends, the microgrid sells the remaining energy to the distribution network company at a set clearing price.

[0062] A second aspect of the present invention provides an analysis system for demand-side load balancing in a power grid, comprising:

[0063] The acquisition module is used to acquire the operating parameters and environmental monitoring data of the power generation system in the microgrid, and to calculate the power generation capacity of the power generation system.

[0064] The energy storage model building module is used to establish a microgrid energy storage model based on the power generation capacity of the power generation system;

[0065] The microgrid power supply model construction module is used to collect the current total stored power and current load demand in the microgrid, and combine the energy storage model to analyze the power supply and demand of the microgrid and construct the microgrid power supply model.

[0066] The pricing strategy generation module is used to construct the objective function of the microgrid power supply model with the aim of maximizing the benefits of the microgrid and distribution network; the pricing strategy is obtained by solving the objective function using a Markov decision model.

[0067] The pricing strategy optimization module uses the expected reward of maximizing total benefits within a set period and optimizes the pricing strategy using the Q-learning algorithm to obtain the microgrid time-of-use pricing strategy.

[0068] The pricing module is used to price the electricity traded between the microgrid and the distribution network through a microgrid time-of-use pricing strategy.

[0069] A third aspect of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed by a processor, implements the steps of the analysis method.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0071] This invention uses the expectation of maximizing total benefit within a set period as the reward, and optimizes the pricing strategy using the Q-learning algorithm to obtain a microgrid time-of-use pricing strategy. The microgrid time-of-use pricing strategy is used to price the electricity traded between the microgrid and the distribution network. Taking into account the interests of multiple parties, a reasonable pricing model and market trading mechanism are established, and the load on the supply and demand sides is balanced by dynamically adjusting the price.

[0072] This invention utilizes the Q-learning algorithm to optimize pricing strategies. Q-learning can learn from past experience, adaptively handle changes in influencing factors, and adjust pricing strategies in real time to obtain a time-of-use pricing strategy for microgrids. This promotes the development of microgrids while ensuring the healthy and stable development of the electricity market. Attached Figure Description

[0073] Figure 1 This is a flowchart of an analysis method for power grid demand-side load balancing provided by an embodiment of the present invention;

[0074] Figure 2 This is a structural diagram of the power grid demand-side load balancing analysis system provided in an embodiment of the present invention;

[0075] Figure 3 This is a flowchart of the generation and optimization of pricing strategies provided in an embodiment of the present invention. Detailed Implementation

[0076] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0077] Example 1

[0078] like Figures 1 to 3 As shown, an analysis method for demand-side load balancing in a power grid includes:

[0079] Methods for obtaining operating parameters and environmental monitoring data of the power generation system in a microgrid, and calculating the power generation capacity of the power generation system, include:

[0080] The solar radiation intensity, temperature, air density, and wind speed in the environment where the microgrid is located are collected as environmental monitoring data; the area of ​​photovoltaic panels, the conversion efficiency of photovoltaic panels, the swept area of ​​wind turbine blades, and the conversion efficiency of wind farms in the power generation system are collected as operating parameters.

[0081] The formula for calculating the power generation of a photovoltaic generator set is as follows:

[0082]

[0083] In the formula, η represents the photovoltaic power generation of the photovoltaic generator in microgrid c during time period t; I represents the solar radiation intensity; S represents the area of ​​the photovoltaic panel; η represents the photovoltaic power generation of the photovoltaic generator in microgrid c during time period t. PV The conversion efficiency of the photovoltaic panel; α PV T is the temperature coefficient; PV The ambient temperature for photovoltaic power generation; T cref This is a reference value for the ambient temperature of photovoltaic power generation.

[0084] The formula for calculating the power generation of a wind turbine generator is as follows:

[0085]

[0086] In the formula, Let C be the wind power generation of the wind turbine generators in microgrid c during time period t; p Power coefficient; A Wind ρ is the swept area of ​​the blades; air η is the air density; v is the wind speed at which the wind turbine in the wind turbine generator is subjected to force; η is the wind speed at which the wind turbine is subjected to force. Wind This refers to the wind farm conversion efficiency of the wind turbine generator set.

[0087] The energy storage model of a microgrid is established based on the power generation capacity of the power generation system, and the formula is as follows:

[0088]

[0089] In the formula, Q t,c Let be the energy storage capacity of the energy storage device in microgrid c during time period t; Δt is the time period step. Let be the wind power generation of the wind turbine generators in microgrid c during time period t; Let t represent the photovoltaic power generation of the photovoltaic generator set in microgrid c during time period t; in this embodiment, the energy storage device is a battery.

[0090] The total stored electricity and current load demand in the microgrid are collected, and the power supply and demand situation of the microgrid is analyzed in conjunction with the energy storage model. The expression formula of the microgrid power supply model is constructed as follows:

[0091]

[0092]

[0093]

[0094] In the formula, This represents the actual discharge amount of the energy storage device in the microgrid during time period t. Q represents the actual discharge of the energy storage device in the microgrid during time period n; n,c This represents the amount of electricity stored in the energy storage device in microgrid c during time period n; This represents the theoretical discharge amount of the energy storage device in the microgrid during time period t. The total electrical charge of the energy storage devices in the microgrid before discharge in time period t; ed t,c The load demand of the microgrid during time period t; ed n,c This represents the load demand of the microgrid during time period n; δ t A represents the discharge efficiency of energy storage devices in a microgrid. max A represents the energy threshold for the initial power supply of the energy storage device. min This represents the energy threshold at which the energy storage device terminates its power supply.

[0095] In electricity trading, electricity retailers set electricity prices, and microgrids dynamically change their load consumption through demand response. The utility function for the total operating cost of the microgrid considers electricity purchase costs, maintenance costs of renewable energy generation equipment, and user dissatisfaction; the utility function for the total profit of the electricity retailers considers electricity sales profits and high-load penalties.

[0096] Microgrids meet their electricity demand in response to demand fluctuations, which are reflected in the price elasticity of demand index ξ. t The formula for expressing the price sensitivity of electricity consumption is:

[0097]

[0098] in

[0099]

[0100] In the formula, ξ t This is the price elasticity of demand index; This refers to the load demand after the battery has discharged; ec t,c This represents the actual load consumed after demand response; v t The cost price for electricity sales companies; μ t,c The electricity price sold by the electricity sales company.

[0101] Based on the price elasticity of demand index ξ t Calculate the actual load consumption ect,c after demand response.

[0102] The objective function of the microgrid power supply model, which aims to maximize the benefits of both the microgrid and the distribution network, is calculated using the following formula:

[0103] max B = β·max B RT +(1-β)·max B MG

[0104] In the formula, max B represents the maximum total benefit of the system; max B MG For the maximum total benefit of the microgrid; max B RT The maximum total benefit for the electricity sales company; β is the transaction weighting coefficient;

[0105] The maximum total benefit of the microgrid is max B. MG The formula for expressing it is:

[0106]

[0107] C MG =ec t,c μ t,c

[0108]

[0109]

[0110]

[0111] In the formula, C MG Electricity purchase cost for microgrids; P represents the satisfaction score of the microgrid during time period t; O For generator set operation and maintenance costs; er t,c α represents the load consumption during time period t. c ζ represents the satisfaction coefficient; μ represents the operation and maintenance cost coefficient; ζ represents the maintenance cost coefficient. t,c The sales price of electricity in the distribution network during time period t; ec t,c This represents the actual load consumed after demand response.

[0112] The maximum total benefit of the electricity sales company is max B. RT The formula for expressing it is:

[0113]

[0114] C RT =ec t,c ·(μ t,c -ν t )

[0115]

[0116] In the formula, C RT For the electricity sales profit of the distribution network; The high-load penalty refers to the risk that the distribution network must bear due to excessive load. The greater the load, the greater the risk and the heavier the penalty for the electricity sales company. χ is the high-load penalty coefficient. The total operating utility function of the microgrid considers electricity purchase costs, renewable energy generation equipment maintenance costs, and user satisfaction. Renewable energy generation equipment maintenance costs include depreciation costs, installation costs, and maintenance costs. User satisfaction refers to the dissatisfaction the microgrid experiences due to high electricity prices and the need to reduce load consumption through demand response.

[0117] Methods for obtaining pricing strategies by solving the objective function using Markov decision models include:

[0118] The microgrid load demand during time period t will be used as the state s of the Markov decision model. t The formula is as follows:

[0119] s t ={ec t,c ,ed t,c}

[0120] The electricity price setting for the distribution network during time period t will be considered as action a in the Markov decision model. t The formula is as follows:

[0121] a t ={μ t,c |μ t,c ∈R,κ1ν t ≤μ t,c ≤κ2ν t}

[0122] Where k1 and k2 are price constraint coefficients.

[0123] Construct the reward r(s) for utility in energy trading within time period t. t ,a t The formula is:

[0124]

[0125] Based on the reward r(s) t ,at Calculate the current value R of future rewards. t The formula is as follows:

[0126] R t =r(s t+1 ,a t+1 )+γ·r(s t+2 ,a t+2 )+γ 2 ·r(s t+3 ,a t+3 )+......+γ T-t-1 ·r(s T ,a T )

[0127] In the formula, r(s) t+1 a t+1 ), r(s) t+2 a t+2 ), r(s) t+3 a t+3 ) and r(s T a T ) represent the utility reward in energy transactions within the time intervals t+1, t+2, t+3, and T, respectively; γ represents the discount factor, and 0≤γ≤1, indicating the importance attached to future rewards; when γ=0, it means that the pricing strategy only focuses on immediate rewards; when γ=1, it means that the pricing strategy believes that performing the same action will bring the same reward each time.

[0128] Based on the current value R of future rewards t Establish a pricing strategy.

[0129] Preferably, the method for optimizing the pricing strategy using the Q-learning algorithm, with the expected reward being the maximum total benefit obtained within a set period, to obtain the time-of-use pricing strategy for microgrids includes:

[0130] Analyze the discharge status of the energy storage device and calculate the load demand of the microgrid; initialize the Q value to 0; at time t, initialize the state information s and observe the load demand of the microgrid;

[0131] The expected reward E(R) is the total benefit obtained within a set period. t The formula is:

[0132] Q k (s t ,a t )=E(R t )

[0133] Q k (s t a t ) represents the state s tNext, execute action a t Q value;

[0134] An ε-greedy strategy is introduced, which randomly selects actions with a probability of ε and selects the action that maximizes the Q value with a probability of 1-ε. The greedy strategy avoids the problem of large errors between the Q value and the actual optimal value when the learning experience is relatively small. The action with the highest Q value will cause the agent to always use the same strategy and will not be able to explore other better values, thus easily getting trapped in the problem of local optima.

[0135] The distribution network selects actions based on the pricing strategy and iteratively updates the Q value, expressed by the following formula:

[0136] Q k+1 (s t ,a t )←Q k (s t ,a t )+θ·[R t +γ·max Q k (s t+1 ,a t )-Q k (s t ,a t )]

[0137] Q' k (s t ,a t ) = R t +γ·maxQ k (s t+1 ,a t )

[0138] In the formula, Q' k (s t ,a t ) refers to taking action a in the k-th iteration. t The actual reward, Q' k (s t ,a t ) and Q k (s t ,a t The difference between Q and θ represents the error between the actual reward and the expected reward in this learning process. This error decreases as learning progresses. θ is the learning rate, where 0 ≤ θ ≤ 1, representing the speed at which Q-values ​​are learned. The state s... t+1 This is expressed as the microgrid load demand during the t+1 time period;

[0139] When the Q value gradually converges to the maximum value, the iteration ends and the optimal microgrid time-of-use pricing strategy at the current moment is output.

[0140] Q*(s t,a t )=max Q(s t ,a t )

[0141] μ*=arg max Q*(s t ,a t )

[0142] In the formula, μ * This represents the optimal time-of-use pricing strategy for the microgrid at the current moment.

[0143] The microgrid uses a time-of-use pricing strategy to price electricity traded between the microgrid and the distribution network; it sets upper and lower limits for the electricity sales price of the distribution network; after the set period ends, the microgrid sells the remaining energy to the distribution network company at the set clearing price; it takes into account the interests of all parties, establishes a reasonable pricing model and market trading mechanism, and balances the load on the supply and demand sides through dynamic price adjustment; it promotes the development of microgrids while ensuring the healthy and stable development of the electricity market.

[0144] After obtaining authorization, the distribution network retrieves smart contract transaction information from the blockchain platform and sells the electricity according to the contract sequence. At the start of a transaction, the microgrid requests the transaction, and both parties confirm the transaction. Once confirmed, a consensus is reached; otherwise, the transaction is restarted until a transaction is completed or terminated. The smart contract data for the completed transaction is uploaded to the blockchain platform and cannot be altered. The microgrid pays the price stipulated in the contract, and after payment, the funds are transferred to the electricity sales company, which then transfers the purchased electricity back to the microgrid.

[0145] Example 2

[0146] An analysis system for demand-side load balancing in a power grid. The evaluation system provided in this embodiment can be applied to the analysis method described in Embodiment 1. The analysis system includes:

[0147] The acquisition module is used to acquire the operating parameters and environmental monitoring data of the power generation system in the microgrid, and to calculate the power generation capacity of the power generation system.

[0148] The energy storage model building module is used to establish a microgrid energy storage model based on the power generation capacity of the power generation system;

[0149] The microgrid power supply model construction module is used to collect the current total stored power and current load demand in the microgrid, and combine the energy storage model to analyze the power supply and demand of the microgrid and construct the microgrid power supply model.

[0150] The pricing strategy generation module is used to construct the objective function of the microgrid power supply model with the aim of maximizing the benefits of the microgrid and distribution network; the pricing strategy is obtained by solving the objective function using a Markov decision model.

[0151] The pricing strategy optimization module uses the expected reward of maximizing total benefits within a set period and optimizes the pricing strategy using the Q-learning algorithm to obtain the microgrid time-of-use pricing strategy.

[0152] The pricing module is used to price the electricity traded between the microgrid and the distribution network through a microgrid time-of-use pricing strategy.

[0153] Example 3

[0154] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the analysis method described in Embodiment 1.

[0155] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0156] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing demand side load balancing of an electrical grid, characterized by, The method comprises the following steps: The operation parameters and environmental monitoring data of the power generation system in the micro-grid are acquired, and the power generation of the power generation system is calculated; An energy storage model of the micro-grid is established based on the power generation of the power generation system; The current total storage power and current load demand in the micro-grid are collected, and the power supply and demand of the micro-grid is analyzed in combination with the energy storage model to construct a micro-grid power supply model; the expression formula is: ； ； ； wherein, is the actual discharge amount of the energy storage device in the micro-grid within the t period; is the actual discharge amount of the energy storage device in the micro-grid within the n period; is the storage amount of the energy storage device in the micro-grid c within the n period; is the theoretical discharge amount of the energy storage device in the micro-grid within the t period; is the total amount of electricity of the energy storage device in the micro-grid before discharging within the t period; is the load demand amount of the micro-grid within the t period; is the load demand amount of the micro-grid within the n period; is the discharge efficiency of the energy storage device in the micro-grid, is the electricity threshold value for the energy storage device to start supplying energy, is the electricity threshold value for the energy storage device to stop supplying energy. A target function of the micro-grid power supply model is constructed for the maximum benefit of the micro-grid and the distribution network, and the calculation formula is: ； In the formula, is the maximum total benefit of the system; is the maximum total benefit of the micro-grid; is the maximum total benefit of the power selling company; is the transaction weight coefficient; The micro-grid maximum total benefit The expression formula is: ； ； ； ； ； In the formula, is the electricity purchase cost of the micro-grid; is the satisfaction score of the micro-grid in the t period; is the operation and maintenance cost of the generator set; is the load consumption in the t period; is the satisfaction coefficient; is the operation and maintenance cost coefficient; is the selling price of the distribution network in the t period; is the actual consumption load after demand response; is the storage capacity of the energy storage device in the micro-grid c in the t period; is the load demand after the battery discharges. The maximum total benefit of the power selling company The expression formula is: ； ； ； In the formula, is the profit of the power distribution network; is the high load penalty; is the high load penalty coefficient; is the cost price of the power selling company; The target function is solved by using a Markov decision model to obtain a pricing strategy; the pricing strategy is optimized by using a Q-learning algorithm to obtain a micro-grid time-of-use pricing strategy, with the maximum total benefit in a set period as the reward expectation; and the electricity traded between the micro-grid and the distribution network is priced by using the micro-grid time-of-use pricing strategy.

2. The method of claim 1, wherein, The method for acquiring the operation parameters and environmental monitoring data of the power generation system in the micro-grid and calculating the power generation of the power generation system comprises the following steps: The solar radiation intensity, temperature, air density and wind speed in the environment where the micro-grid is located are collected as the environmental monitoring data; and the area of the photovoltaic panel, the conversion efficiency of the photovoltaic panel, the blade sweep area of the wind turbine generator set and the wind power field conversion efficiency in the power generation system are collected as the operation parameters; The power generation of the photovoltaic generator set is calculated, and the expression formula is: ； In the formula, is the photovoltaic power generation power of the photovoltaic generator set in the micro-grid c at the t period; is the solar radiation intensity; S is the photovoltaic panel area; is the conversion efficiency of the photovoltaic panel; is the temperature coefficient; is the ambient temperature of the photovoltaic power generation; is the reference value of the ambient temperature of the photovoltaic power generation; The power generation of the wind turbine generator set is calculated, and the expression formula is: ； In the formula, is the wind power generation power of the wind turbine generator set in the micro-grid c in the time period t; is the power coefficient; is the blade swept area; is the air density; is the forced wind speed of the wind turbine in the wind turbine generator set; is the wind farm conversion efficiency of the wind turbine generator set.

3. The method of claim 2, wherein, An energy storage model of the micro-grid is established based on the power generation of the power generation system, and the expression formula is: ； In the formula, is the storage capacity of the energy storage device in the microgrid c at the time period t; is the time period step; is the wind power generation capacity of the wind turbine generator set in the microgrid c at the time period t; is the photovoltaic power generation capacity of the photovoltaic generator set in the microgrid c at the time period t.

4. The method of claim 1, wherein, The method for solving the target function by using the Markov decision model to obtain the pricing strategy comprises the following steps: The load demand of the micro-grid in a t period is taken as a state of a Markov decision model The electricity price formulated by the distribution network in the t period is taken as an action of the Markov decision model The reward of the utility in the energy transaction in the t period is constructed The expression formula is: ； According to the reward Computing the value of future rewards at the present , expressed as: ； In the formula, is expressed as a discount factor; , , and represent the reward of utility in energy transactions within the t+1, t+2, t+3 and T, respectively. Value of future rewards at present Establish pricing strategy.

5. The method of claim 4, wherein, The method for optimizing the pricing strategy by using the Q-learning algorithm to obtain the micro-grid time-of-use pricing strategy, with the maximum total benefit in a set period as the reward expectation, comprises the following steps: To set the period within the acquisition of the maximum total benefit as reward expectations The expression formula is: ； Q value of the action performed at state the action performed at state The power distribution network selects an action according to a Q value and a greedy strategy And the Q value is iteratively updated, and the expression formula is: ； In the formula, is the learning rate, i.e., the speed of Q value learning; state represents the micro-grid load demand amount in the t+1 period; When the Q value gradually converges to the maximum value, the iteration is ended, and the optimal micro-grid time-of-use pricing strategy at the current time is outputted. ； ； In the formula, The current time optimal micro-grid time-sharing pricing strategy is represented.

6. The method of claim 1 or claim 5, wherein, Further, the electricity selling price of the distribution network is set to have an upper limit and a lower limit; and after the set period ends, the micro-grid sells the remaining energy to the distribution network company at the set clearing price.

7. An analysis system for demand side load balancing of an electrical grid, characterized in that The method comprises the following steps: An acquisition module is configured to acquire the operation parameters and environmental monitoring data of the power generation system in the micro-grid, and calculate the power generation of the power generation system; An energy storage model construction module is configured to establish an energy storage model of the micro-grid based on the power generation of the power generation system; A micro-grid power supply model construction module is configured to collect the current total storage power and current load demand in the micro-grid, and analyze the power supply and demand of the micro-grid in combination with the energy storage model to construct a micro-grid power supply model; A pricing strategy generation module is configured to construct a target function of the micro-grid power supply model for the maximum benefit of the micro-grid and the distribution network; and solve the target function by using a Markov decision model to obtain a pricing strategy; A pricing strategy optimization module is configured to optimize the pricing strategy by using a Q-learning algorithm, with the maximum total benefit in a set period as the reward expectation, to obtain a micro-grid time-of-use pricing strategy; A pricing module is configured to price the electricity traded between the micro-grid and the distribution network by using the micro-grid time-of-use pricing strategy; The expression formula of the micro-grid power supply model is ； ； ； wherein, is the actual discharge amount of the energy storage device in the micro-grid within the t period; is the actual discharge amount of the energy storage device in the micro-grid within the n period; is the storage amount of the energy storage device in the micro-grid c within the n period; is the theoretical discharge amount of the energy storage device in the micro-grid within the t period; is the total amount of electricity of the energy storage device in the micro-grid before discharging within the t period; is the load demand amount of the micro-grid within the t period; is the load demand amount of the micro-grid within the n period; is the discharge efficiency of the energy storage device in the micro-grid, is the electricity threshold value at which the energy storage device starts to supply energy, is the electricity threshold value at which the energy storage device stops to supply energy. The pricing strategy generation module constructs a target function of a micro-grid power supply model for the purpose of maximum benefit of the micro-grid and the power distribution network, and the calculation formula is: ； In the formula, is the maximum total benefit of the system; is the maximum total benefit of the micro-grid; is the maximum total benefit of the power selling company; is the transaction weight coefficient; The micro-grid maximum total benefit The expression formula is: ； ； ； ； ； In the formula, is the electricity purchase cost of the micro-grid; is the satisfaction score of the micro-grid in the t period; is the operation and maintenance cost of the generator set; is the load consumption in the t period; is the satisfaction coefficient; is the operation and maintenance cost coefficient; is the selling price of the distribution network in the t period; is the actual consumption load after demand response; is the storage capacity of the energy storage device in the micro-grid c in the t period; is the load demand after the battery discharges. The maximum total benefit of the power selling company The expression formula is: ； ； ； In the formula, is the profit of the power distribution network; is the high load penalty; is the high load penalty coefficient; is the cost price of the power selling company.

8. A computer readable storage medium, characterized in that, Stored on it is a computer program which, when executed by a processor, implements the steps of the analysis method of any one of claims 1 to 6. Stored on it is a computer program which, when executed by a processor, implements the steps of the analysis method of any one of claims 1 to 6.

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