A multi-objective energy management model and method

Abstract

The embodiment of the present disclosure relates to the technical field of energy management, and discloses a multi-target energy management model and method, the method comprising: a distribution system operator scheduling a distribution network based on minimum operation cost, and clearing a day-ahead transaction market to obtain a market clearing result; and a virtual power plant obtaining demand response from internal and external demand response suppliers based on the market clearing result and maximum expected income. By using the exemplary embodiment of the present disclosure, on the one hand, through a demand response transaction market, the virtual power plant can reduce its energy transaction in the power market, so that the dependence of the virtual power plant on the network is alleviated; in addition, the imbalance penalty of the virtual power plant can be reduced, and the network congestion of the power distribution system can be alleviated; on the other hand, the distribution system operator schedules the distribution network and clears the two markets, so that the operation cost is minimized and the social welfare is maximized.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of energy management, and in particular, to a multi-objective energy management model and method. BACKGROUND

[0002] With strong economic support and social policy, renewable energy generation has increased rapidly, but their randomness brings many challenges to the stable operation of power systems. In order to cope with these challenges, virtual power plants have emerged, which integrate distributed generators into individuals participating in the electricity market or providing system support services.

[0003] At present, many researchers have proposed different control strategy models for virtual power plants, including risk-constrained stochastic models, double-decision models, etc. In order to participate in demand response, some experts propose to introduce distributed energy generation into the electricity market, prepare for direct transactions between buyers and sellers, effectively manage the intermittency of renewable energy generation, and reduce electricity purchase costs.

[0004] Due to the intermittent and random characteristics of uncertain resources of virtual power plants, such as renewable energy generators, demand loads and market prices, virtual power plants may not be able to configure energy for day-ahead settlement in the day-ahead trading market, resulting in a deviation between the power settled in the day-ahead trading market and the real-time dispatch of virtual power plants. Therefore, virtual power plants may face unbalanced fines from regulatory markets, which will result in a serious reduction in their profits.

[0005] In addition, how to optimize the procurement expenditure of the trading market, how to improve the expected revenue of the virtual power plant, and how to maximize social welfare are also problems to be solved. SUMMARY

[0006] Embodiments of the present disclosure provide a multi-objective energy management model and method to solve or alleviate one or more of the above technical problems in the prior art.

[0007] According to one aspect of the present disclosure, a multi-objective energy management method is provided, comprising:

[0008] The allocation system operator schedules the allocation network based on minimum operation cost and settles the day-ahead trading market to obtain a market settlement result;

[0009] The virtual power plant obtains demand response from internal and external demand response suppliers based on the market settlement result and maximum expected revenue.

[0010] In one possible implementation, the virtual power plant obtains demand response from internal and external demand response suppliers based on the market settlement result and maximum expected revenue, comprising:

[0011] The virtual power plant obtains the elastic demand response of the internal demand response supplier adjusted according to the bidding signal;

[0012] The virtual power plant obtains the demand response service of the external demand response supplier through the demand response transaction market;

[0013] The virtual power plant makes a decision based on the bid of the internal demand response supplier, the bid of the internal demand response supplier, and the regulatory market price.

[0014] In a possible implementation, the virtual power plant obtains the demand response service of the external demand response supplier through the demand response transaction market, including:

[0015] According to the result of market clearing, the virtual power plant sends a call signal to the demand response transaction market to sell excess power or buy short power;

[0016] The external demand response supplier receives the call signal through the demand response transaction market, and provides the demand response service to the virtual power plant according to the received call signal.

[0017] In a possible implementation, according to the result of market clearing, the virtual power plant sends a call signal to the demand response transaction market to sell excess power or buy short power, including:

[0018] When the total actual output of the virtual power plant exceeds the settled power on the day-ahead transaction market, the virtual power plant sells its excess power in the demand response transaction market at a price higher than the downward adjustment price;

[0019] When the total actual output power of the virtual power plant is lower than the settled power on the day-ahead transaction market, the virtual power plant buys short power in the demand response transaction market at a price lower than the upward adjustment price.

[0020] In a possible implementation, in the process that the external demand response supplier receives the call signal through the demand response transaction market, and provides the demand response service to the virtual power plant according to the received call signal, the load provided by the external demand response supplier to the demand response transaction market is:

[0021]

[0022] In formula (1), q j represents the total load that needs to be reduced or increased; represents the choices of the external demand response supplier that can provide load reduction for the virtual power plant, including load reduction, load shifting, and utilization of on-site energy storage; represents the choice of the external demand response supplier to increase the load and benefit by adjusting the cost function; j represents the jth external demand response supplier; s represents the case of reducing the load; D represents the case of increasing the load; and n represents the total number of external demand response suppliers.

[0023] In a possible implementation, when the total actual output of the virtual power plant exceeds the settlement power of the virtual power plant on the day-ahead trading market, the external demand response supplier increases the load, and the cost function of the bid of the external demand response supplier providing the demand response service to the virtual power plant is:

[0024]

[0025] the price λ of the demand response trading market D is:

[0026]

[0027] or, when the external demand response supplier increases the load, the bid price λ D of the increased load is:

[0028] λ D = ψ1λ X ; (4)

[0029] In formulae (2)-(4), λ D represents the price of the demand response trading market; j represents the jth external demand response supplier; n represents the total number of external demand response suppliers; D represents the case of increasing the load; and P j D represents the power of the increased demand response service; represents the choice of the external demand response supplier to increase the load and benefit by adjusting the cost function; ψ1 is a parameter of the increased price; λ x represents the decreased price; and X represents the case of decreasing the power on the regulatory market.

[0030] In a possible implementation, when the total actual output power of the virtual power plant is lower than the settlement power of the virtual power plant on the day-ahead trading market, the utility function of the bid of the external demand response supplier to reduce the load on the day-ahead trading market is:

[0031]

[0032] the bid price λ s of the external demand response supplier is determined by the bidding of the external demand response supplier:

[0033]

[0034] Or, when the external demand response supplier j provides a load reduction demand of , the bidding price λ s of the external demand response supplier is

[0035] λ s = ψ2λ u ; (7)

[0036] In formulae (5)-(7), λ s represents the bidding price of the external demand response supplier, P j s represents the power reduced by the external demand response supplier when the bidding price is λ s ; j represents the jth external demand response supplier; n represents the total number of external demand response suppliers; S represents the load reduction condition; represents the load reduction demand provided by the external demand response supplier; ψ2 is a parameter of price reduction; λ u represents the price increase; and u represents the condition of increasing power on the regulatory market.

[0037] In a possible implementation, the calculation formula of the market inefficiency index is:

[0038]

[0039] In formula (8), SW represents social welfare; ψ 1 / 2 is a parameter value of price reduction or increase; and ψ′ 1 / 2 is a standard value of the parameter value of price reduction or increase.

[0040] In a possible implementation, the expression of the expected revenue maximization is:

[0041] Max∑ w∈Ω π w ∑ t∈T Rev A +Rev N +Rev W -Pen M ; (9)

[0042]

[0043]

[0044]

[0045]

[0046] In formulae (9)-(13), w represents the multi-objective revenue maximization in one scenario; Ω represents multiple target application scenarios; πw represents the probability of occurrence of scenario w; t represents the duration of scenario w; T represents the total time of duration of scenario w; W represents the external demand response part; A represents the day-ahead part; N represents the internal demand response part; M represents the regulatory market part; s represents the case of load reduction; D represents the case of load increase; n represents bus n; X represents the case of regulatory market down power; u represents the case of regulatory market up power; Rev A represents the expected profit of the virtual power plant, including the income of selling the amount of energy settled on the day-ahead trading market; Rev N and Rev W respectively represent the income of energy transaction with the internal demand response supplier and the external demand response supplier; Pen M represents the penalty cost of participating in the regulatory market; represents the day-ahead settled power of the virtual power plant; λ n,t is the marginal price of bus n; is the power provided by the internal demand response supplier; is the specified price of the internal demand response supplier; is the increased power of the external demand response supplier; is the increased price of the external demand response supplier; is the reduced power of the external demand response supplier; is the reduced price of the external demand response supplier; is the down power of the regulatory market; is the down price of the regulatory market; is the up power of the regulatory market; is the up price of the regulatory market.

[0047] In one possible implementation, the increased price of the external demand response supplier is:

[0048]

[0049] The reduced price of the external demand response supplier is:

[0050]

[0051] The day-ahead settled power of the virtual power plant is:

[0052]

[0053] In the formulas (14)-(16), D represents the case of increasing load; t represents the duration of the scenario w; w represents the case of maximizing multi-objective profit in one scenario; ψ1 represents the parameter of price increase; X represents the case of down-regulating power in the regulated market; s represents the case of reducing load; represents the down-regulated price of the regulated market; ψ2 represents the parameter of price decrease; u represents the case of up-regulating power in the regulated market; represents the up-regulated price of the regulated market; A represents the day-ahead part; represents the total rated power of the wind turbine; W represents the external demand response part; represents the power purchased from the demand response trading market; represents the up-regulated power of the regulated market; N represents the internal demand response part; represents the power provided by the internal demand response supplier; represents the power provided by the external demand response; represents the down-regulated power of the regulated market;

[0054] The total rated power of the wind turbine is obtained from the wind speed scenario based on the wind power curve by piecewise linear approximation calculation of the active power of the wind turbine

[0055]

[0056] In the formula (17), P represents the Pth wind power supplier; t represents the duration of the scenario w; w represents the case of maximizing multi-objective profit in one scenario; W represents the external demand response part; v w , v r , v in , and v out respectively represent the wind speed, the rated speed of the wind turbine, the cut-in speed, and the cut-out speed in each scenario, refers to the total rated power of the wind turbine; represents the rated power of the wind turbine; r represents the rated operation case;

[0057] The demand response service purchased or sold by the virtual power plant from the demand response trading market is limited to be less than the maximum value of the demand response service provided by the external demand response supplier:

[0058]

[0059]

[0060] In the formulas (18)-(19), t represents the duration of the scenario w; w represents the case of maximizing multi-objective profit in one scenario; D represents the case of increasing load; W represents the external demand response part; s represents the case of reducing load; the maximum power provided by the external demand response provider, denotes the maximum power purchased from the demand response trading market; denotes the power provided by the external demand response; denotes the power purchased from the demand response trading market.

[0061] In one possible implementation, the expression for minimizing the operating cost is:

[0062] Min∑ w∈Ω π w ∑ t∈T [Cost G +Cost VPP -Income D ]; (20)

[0063]

[0064]

[0065]

[0066] In equations (20)-(23), w denotes the case of maximizing multi-objective income in a scenario; Ω denotes multiple target application scenarios; π w denotes the occurrence probability of scenario w; t denotes the duration of scenario w; T denotes the total time of the duration of scenario w; Cost π denotes the cost paid by the distribution system operator to the generator set; Cost VPP denotes the cost paid by the distribution system operator to the virtual power plant; Income D denotes the income obtained by the distribution system operator by selling energy to the load; is the power of the generator set, is the marginal cost of the generator set, α t is the specified price of the virtual power plant; denotes the day-ahead settlement power of the virtual power plant; is the increased power of the external demand response provider; is the increased price of the external demand response provider; G denotes the generator set; VPP denotes the virtual power plant; D denotes the increased load; A denotes the day-ahead part.

[0067] In one possible implementation, it further includes:

[0068] According to the market inefficiency index, the rate of change of the social welfare of the distribution system operator when the virtual power plant participates or does not participate in the demand response trading market is quantitatively evaluated.

[0069] In a possible implementation, the virtual power plant comprises a renewable energy power generating unit and a plurality of groups of internal demand response devices;

[0070] The renewable energy power generating unit comprises a wind turbine and a dispatchable power generating unit;

[0071] Each group of the internal demand response devices comprises responsive and non-responsive loads, and the internal demand response devices are configured to provide demand response services for the virtual power plant.

[0072] According to one aspect of the present disclosure, a multi-objective energy management model is provided, comprising: a distribution system operator, a day-ahead market, a virtual power plant, an internal demand response provider, and an external demand response provider;

[0073] The distribution system operator is configured to schedule a distribution network based on minimum operation cost, and to clear the day-ahead market to obtain a market clearing result;

[0074] The virtual power plant is configured to obtain demand response from the internal demand response provider and the external demand response provider based on maximum expected revenue according to the market clearing result.

[0075] In a possible implementation, further comprising a demand response trading market and a regulatory market;

[0076] The internal demand response provider is configured to adjust responsive loads according to a bidding signal to provide flexible demand response for the virtual power plant;

[0077] The external demand response provider is configured to provide demand response services for the virtual power plant through the demand response trading market;

[0078] The virtual power plant is configured to make a decision based on the bidding of the internal demand response provider, the bidding of the internal demand response provider, and a price of the regulatory market.

[0079] In a possible implementation, the virtual power plant is configured to send a calling signal to the demand response trading market to sell excess power or purchase insufficient power according to the market clearing result;

[0080] The external demand response provider is configured to receive the calling signal through the demand response trading market, and to provide demand response services for the virtual power plant according to the received calling signal.

[0081] In a possible implementation, the virtual power plant is configured to sell excess power at a price higher than a downward adjustment price in the demand response trading market when a total actual output of the virtual power plant exceeds a settlement power of the virtual power plant in the day-ahead market;

[0082] Also used for when the actual total output power of the virtual power plant is lower than its settlement power on the day-ahead trading market, to purchase the short power in the demand response trading market at a price lower than the up-regulation price.

[0083] The exemplary embodiments disclosed have the following beneficial effects: with the exemplary embodiments of the disclosure, on the one hand, through the demand response trading market, the virtual power plant can reduce its energy trading in the electricity market, so that the dependence of the virtual power plant on the network is alleviated; in addition, under the demand response trading market environment, demand response plans are implemented for internal demand response, and interaction with external demand response suppliers can reduce the imbalance penalty of the virtual power plant, and the demand flexibility of demand response services such as load reduction or increase, and thus the supply of local load during peak hours can alleviate the network congestion of the power distribution system; on the other hand, the distribution system operator schedules the distribution network and clears the two markets, which can minimize the operating cost and maximize the social welfare.

[0084] The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features and advantages of the application will be apparent from the description of the specification, drawings and claims. It should be understood that the foregoing general description and the following detailed description are merely exemplary and explanatory, and are not restrictive of the disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0085] The drawings herein are incorporated into the specification and constitute a part of the specification, show embodiments consistent with the disclosure, and together with the specification serve to explain the principles of the disclosure. It is obvious that the drawings in the following description are only some embodiments of the disclosure, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0086] Figure 1 is a structural schematic diagram of a multi-objective energy management model of the exemplary embodiments. DETAILED DESCRIPTION

[0087] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any of various forms, and should not be limited to the examples described herein; rather, the example implementations can be implemented in any number of forms. Like reference numerals can be used to denote like elements throughout the following description. Various implementations of the disclosure can be implemented to realize one or more of the following examples. The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting; rather, the scope of the disclosure is defined by the appended claims. For purposes of illustration, the example implementations are described herein primarily in the context of a virtual power plant (VPP) and a distribution system operator (DSO). However, the example implementations can be implemented in any number of other contexts.

[0088] Figure 1 is a structural diagram of a multi-objective energy management model of the present example embodiment, based on Figure 1 Based on the multi-objective energy management model shown in

[0089] The distribution system operator schedules the distribution network based on minimum operation cost, and clears the day-ahead market to obtain a market clearing result;

[0090] The virtual power plant obtains demand response from internal demand response suppliers and external demand response suppliers based on maximum expected revenue, according to the market clearing result.

[0091] In the present example embodiment, a bi-level decision model is established by a probabilistic mixed integer linear programming method, and the mutual relationship between the virtual power plant as a price maker and the distribution system operator is analyzed.

[0092] Due to the intermittent and random characteristics of uncertain resources of the virtual power plant, such as renewable energy generators, demand loads, and market prices, the virtual power plant can not be able to configure energy for day-ahead clearing on the day-ahead market. Therefore, the virtual power plant can face an imbalance penalty on the regulatory market, which will lead to a significant reduction in its profit. In the present example embodiment, in order to avoid the negative impact of uncertain resources on the profit of the virtual power plant, the virtual power plant makes up for its power deviation through two demand response resources. In the demand response trading market, based on the two options of internal demand response resources and external demand response resources, the uncertainty deviation of the virtual power plant can be reduced, leading to a reduction in the adjustment penalty.

[0093] In exemplary embodiments, the virtual power plant has a strategic position in the network and acts as a price maker, and thus it can influence the market clearing price. Under these conditions, the virtual power plant maximizes its expected revenue by using the flexibility of the internal demand response and participating in the demand response trading market to modify the market clearing price and prevent imbalance penalty payments.

[0094] During the clearing market, the dispatch system operator performs the optimal power flow calculation and provides the market clearing results to the virtual power plant and other agents. It should be noted that since the dispatch system operator does not own the generation units, the external demand response suppliers, or the virtual power plant, it will stimulate the virtual power plant to obtain the market clearing bids by publishing prices. The amount of power exchange with the virtual power plant and the distribution system is achieved through the economic dispatch process at the given prices.

[0095] Exemplarily, the virtual power plant includes renewable energy generation units and several groups of internal demand response devices;

[0096] The renewable energy generation units include wind turbines and dispatchable generation units;

[0097] Each group of internal demand response devices includes responsive and non-responsive loads, and the internal demand response devices are used to provide demand response services for the virtual power plant.

[0098] In the exemplary embodiments, the virtual power plant is composed of some wind turbines as renewable energy generation units, dispatchable generation units, and some flexible loads as internal demand response. Each group of internal demand response includes several responsive and non-responsive loads, and can provide demand response services for the virtual power plant.

[0099] In addition, in order to reduce the cost of deviation between the day-ahead trading market clearing power and real-time dispatch, the virtual power plant can obtain demand response services from external demand response suppliers in the demand response trading market environment.

[0100] The external demand response suppliers are more flexible, and can provide upward demand response resources by reducing the responsive load, or provide downward demand response resources by increasing consumption.

[0101] Exemplarily, the virtual power plant obtains demand response from internal demand response suppliers and external demand response suppliers based on the market clearing results, and maximizes the expected revenue based on the expected revenue maximization, including:

[0102] The virtual power plant obtains the elastic demand response of the internal demand response suppliers adjusted according to the bidding signal;

[0103] The virtual power plant obtains the demand response services of the external demand response suppliers through the demand response trading market;

[0104] The virtual power plant makes decisions based on the bids of the internal demand response providers, the bids of the internal demand response providers and the regulated market price.

[0105] In the present exemplary embodiment, to provide demand response services, internal demand response is modeled using flexible demand and bid signals. In fact, according to the bid signals, capable internal demand response will adjust their responsive loads and provide flexible demand response to the virtual power plant.

[0106] In addition, external demand response providers submit their bids in the trading market to provide demand response services. The bid signals of the external demand response will be collected and sent to the virtual power plant through the local virtual response trading market (demand response trading market). This requires intensive computing power and communication infrastructure; import / export price data is transmitted through smart meters. The demand response trading market can provide demand response options for the virtual power plant to purchase demand response services through this local market.

[0107] The decision of the virtual power plant is made by considering the bids of the external demand response providers, the bids of the internal demand response, the bids of the loads and the marginal prices of the generator units, and the regulated market price.

[0108] In the present exemplary embodiment, both the internal demand response provided by the internal demand response providers and the local virtual response trading market are used to describe the deviation of the virtual power plant between the power of the day-ahead trading market settlement and the real-time dispatch. This problem is converted into a bi-level model problem, in which the virtual power plant tries to maximize its expected revenue in the upper layer, and the distribution system operator seeks to maximize the social welfare by clearing the two markets (the internal demand response trading market and the external demand response trading market) in the lower layer.

[0109] Exemplarily, the virtual power plant acquires the demand response services of the external demand response providers through the demand response trading market, including:

[0110] According to the results of market clearing, the virtual power plant sends a call signal to the demand response trading market to sell excess power or buy short power;

[0111] The external demand response providers receive the call signal through the demand response trading market, and provide demand response services to the virtual power plant according to the received call signal.

[0112] Exemplarily, according to the results of market clearing, the virtual power plant sends a call signal to the demand response trading market to sell excess power or buy short power, including:

[0113] When the total actual output of the virtual power plant exceeds its settlement power on the day-ahead market, the virtual power plant sells its excess power at a price higher than the downward adjustment price in the demand response trading market;

[0114] When the total actual output of the virtual power plant is less than its settlement power on the day-ahead market, the virtual power plant purchases the short power at a price lower than the upward adjustment price in the demand response trading market.

[0115] In the example embodiment, the virtual power plant participates in the day-ahead market as a price maker, and optimizes the procurement expenditure of the demand response market by scheduling internal demand response. When the scheduling power of the virtual power plant is less than the settlement amount on the day-ahead market, the bid of the external demand response supplier is submitted as a supply energy; when the scheduling power of the virtual power plant is larger, the bid of the external demand response supplier is submitted as a demand energy.

[0116] This decision model can solve the influence of the bidding behavior of internal and external demand response suppliers on the decision of the virtual power plant, and introduce the influence of the demand response trading market on the virtual power plant.

[0117] In the example embodiment, when the total actual output of the virtual power plant exceeds their settlement power on the day-ahead market, the virtual power plant can sell its excess power at a price higher than the downward adjustment price in the demand response trading market. On the other hand, when the total actual output of the virtual power plant is less than its settlement power on the day-ahead market, it can purchase the short power at a price lower than the upward adjustment price in the demand response trading market. From a single perspective, through the demand response trading market, the virtual power plant can reduce its energy transactions on the power market, which means that the dependence of the virtual power plant on the network is alleviated. In addition, under the demand response trading market environment, demand response plans are implemented for internal demand response, and interact with external demand response suppliers, which not only reduces the imbalance penalty of the virtual power plant, but also has a serious impact on the grid condition. In fact, the demand flexibility of providing demand response services such as reducing or increasing load, and therefore the supply of local load during peak hours, can alleviate the network congestion of the power distribution system.

[0118] After the market settlement problem is solved, the market settlement price is determined, and the share of the external demand response supplier in the demand response service will also be determined. Then, the external demand response supplier receives a call signal from the virtual power plant and determines their support for meeting the virtual power plant's request.

[0119] Exemplarily, in the process that the external demand response supplier receives a call signal through the demand response trading market and provides a demand response service to the virtual power plant according to the received call signal, the load amount provided by the external demand response supplier to the demand response trading market is:

[0120]

[0121] In formula (1), q j represents the total load that needs to be reduced or increased; represents the option that the external demand response supplier can provide the virtual power plant with load reduction, including load reduction, load shifting, and use of on-site energy storage; represents the option that the external demand response supplier can increase the load by adjusting the cost function and benefit; j represents the jth external demand response supplier; s represents the case of load reduction; D represents the case of load increase; n represents the total number of external demand response suppliers.

[0122] In the present exemplary embodiment, considering that there are some external loads under the jurisdiction of the transaction market of the external demand response supplier, represents the amount of load that the external demand response supplier tends to adjust at the demand response transaction layer. In order to meet the energy deviation of the virtual power plant, the total load that needs to be reduced or increased (q j ) is shown in formula (1).

[0123] Exemplarily, when the total actual output of the virtual power plant exceeds the settlement electricity quantity thereof in the day-ahead transaction market, the external demand response supplier increases the load, and the cost function of the bid of the external demand response supplier to the virtual power plant for the demand response service is:

[0124]

[0125] The price λ D of the demand response transaction market is:

[0126]

[0127] Or, when the external demand response supplier increases the load thereof, the bid price λ D of the increased load is represented as:

[0128] λ D = ψ1λ X (4)

[0129] In formulas (2)-(4), λ D represents the price of the demand response transaction market; j represents the jth external demand response supplier; n represents the total number of external demand response suppliers; D represents the case of load increase; P j D represents the power of the increased demand response service; represents the option that the external demand response supplier can increase the load by adjusting the cost function and benefit; ψ1 is a parameter of price increase; X represents the case of down-regulated power under the regulatory market; λX indicates a downward adjustment of the price.

[0130] It is worth mentioning that formula (3) is a general calculation formula of the transaction market price, cost divided by power. Formula (4) is a price set by the external demand response supplier. Formula (4) reflects the role of the external demand response supplier in regulating the price.

[0131] Exemplarily, when the actual total output power of the virtual power plant is lower than its clearing power on the day-ahead transaction market, the utility function of the external demand response supplier bidding to reduce the load on the day-ahead transaction market is:

[0132]

[0133] The bidding price λ s of the external demand response supplier is determined by the bidding of the external demand response supplier:

[0134]

[0135] When the load reduction demand provided by the external demand response supplier j is , the bidding price λ s of the external demand response supplier is:

[0136] λ s = ψ2λ u ; (7)

[0137] In formula (5)-(7), λ s represents the bidding price of the external demand response supplier, P j s represents the power reduced by the external demand response supplier when the bidding price is λ s ; j represents the jth external demand response supplier; n represents the total number of external demand response suppliers; S represents the load reduction condition; represents the load reduction demand provided by the external demand response supplier; ψ2 is a parameter of price reduction; λ u represents an upward adjustment of the price; u represents the upward adjustment of the power on the regulatory market.

[0138] Exemplarily, in order to quantitatively evaluate the influence of the virtual power plant participating in the demand response transaction market on the market transaction, an evaluation index is defined in this exemplary embodiment. This index is called market inefficiency index (MII), which represents the rate of change of the social welfare of the distribution system operator when the virtual power plant participates in or does not participate in the demand response transaction market. The calculation formula of the market inefficiency index is:

[0139]

[0140] In formula (8), SW represents social welfare; ψ 1 / 2 is a parameter value of price reduction or increase; ψ' 1 / 2 is a standard value of the parameter value of price reduction or increase. Through this index, the allocation system operator can more accurately estimate the market power of the virtual power plant and the social welfare under different conditions. In the MII, the deviation between the social welfare of the allocation system operator under a specific price increase or decrease condition and the standard value is compared.

[0141] Exemplarily, the model for determining the optimal supply strategy of the price maker virtual power plant proposed in the exemplary embodiment is expressed as a double-level stochastic problem. In the upper layer of the problem, the virtual power plant implements a demand response plan for internal demand response and interacts with external demand response suppliers in a demand response transaction market environment to reduce its imbalance penalty. In addition, the virtual power plant covers its uncertainty in the regulatory market and maximizes its expected revenue within the dispatch range. The expression of the expected revenue maximization is:

[0142] Max∑ w∈Ω π w ∑ t∈T Rev A +Rev N +Rev W -Pen M ; (9)

[0143]

[0144]

[0145]

[0146]

[0147] In formulas (9)-(13), w represents the case of multi-objective revenue maximization under a scenario; Ω represents multiple target application scenarios; π w represents the occurrence probability of the scenario w; t represents the duration of the scenario w; T represents the total time of the duration of the scenario w; W represents the external demand response part; A represents the day-ahead part; N represents the internal demand response part; M represents the regulatory market part; s represents the case of load reduction; D represents the case of load increase; n represents bus n; X represents the case of regulatory market down-regulation power; u represents the case of regulatory market up-regulation power; Rev A represents the expected profit of the virtual power plant, including the income of the amount of energy sold for settlement in the day-ahead transaction market; Rev N and Rev W respectively represent the income of energy transaction with the internal demand response supplier and the external demand response supplier; Pen MPfpenal represents the cost of penalties for participating in the regulated market; Pfvirtual represents the virtual power plant's day-ahead settlement power; λ n,t Pfmargin represents the marginal price for bus n; Pfinternal represents the power provided by the internal demand response providers; Pfinternal represents the specified price for the internal demand response providers; Pfexternal represents the incremental power for the external demand response providers; Pfexternal represents the incremental price for the external demand response providers; Pfexternal represents the decremental power for the external demand response providers; Pfexternal represents the decremental price for the external demand response providers; Pfdown represents the down-regulation power for the regulated market; Pfdown represents the down-regulation price for the regulated market; Pfup represents the up-regulation power for the regulated market; Pfup represents the up-regulation price for the regulated market.

[0148] Exemplarily, the demand response trading market's incremental and decremental price definitions for trading energy are products of the regulated market price, the external demand response providers' incremental price Pfexternal is:

[0149]

[0150] Pfexternal is the decremental price for the external demand response providers Pfexternal is:

[0151]

[0152] From equation (14), it can be seen that when the external demand response providers increase energy, the virtual power plant will receive a revenue equal to the down-regulation price. From equation (15), when the external demand response providers decrease energy, the virtual power plant will pay the external demand response providers at the up-regulation price.

[0153] The virtual power plant's day-ahead settlement power will be obtained from the power balance equation given in equation (16) below, which consists of two main parts. The first part includes the predicted wind power Pfexternal represents the power purchased from the demand response trading market and the power purchased from the regulated market The second part includes the internal demand response power of the demand response trading market the power provided by the external demand response and the power sold in the underlying regulated market The virtual power plant's day-ahead settlement power Pfvirtual is:

[0154]

[0155] In formula (14)-(16), D represents the case of increasing load; t represents the duration of scenario w; w represents the case of maximizing multi-objective profit in one scenario; ψ1 represents the parameter of price increase; X represents the case of down-regulating power in the regulated market; s represents the case of reducing load; represents the down-regulated price of the regulated market; ψ2 represents the parameter of price decrease; u represents the case of up-regulating power in the regulated market; represents the up-regulated price of the regulated market; A represents the day-ahead part; represents the total rated power of the wind turbine; W represents the external demand response part; represents the power purchased from the demand response trading market; represents the up-regulated power of the regulated market; N represents the internal demand response part; represents the power provided by the internal demand response supplier; represents the power provided by the external demand response; represents the down-regulated power of the regulated market;

[0156] In addition, the uncertainty of wind power generation is extracted based on wind speed, which can be obtained from wind speed scenarios based on wind power curves by piecewise linear approximation of wind turbine active power, and the total rated power of the wind turbine is calculated from wind speed scenarios based on wind power curves by piecewise linear approximation of wind turbine active power

[0157] In formula (17), P represents the Pth wind power supplier; t represents the duration of scenario w; w represents the case of maximizing multi-objective profit in one scenario; W represents the external demand response part; v w , v r , v in , and v out respectively represent the wind speed, rated speed, cut-in speed, and cut-out speed of the wind turbine in each scenario, denotes the total rated power of the wind turbine; represents the rated power of the wind turbine; r represents the rated operation case;

[0158] It is worth noting that each scenario includes scenario 1: the wind speed is greater than the cut-out speed or less than the cut-in speed; scenario 2: the wind speed is between the cut-in speed and the rated speed; scenario 3: the wind speed is between the rated speed and the cut-out speed.

[0159] The cut-in wind speed refers to the wind speed that meets the grid-connected condition, that is, the lowest wind speed that can generate power, and below this wind speed the wind turbine will automatically shut down.

[0160] The maximum wind speed at which the wind speed indicator wind turbine is cut off from the grid, and above which the turbine is cut off from the grid, i.e. the turbine stops and stops generating electricity, is related to the aerodynamic performance of the blades.

[0161] The demand response service purchased or sold by the virtual power plant from the demand response trading market is limited to be less than the maximum value of the demand response service provided by the external demand response supplier:

[0162]

[0163]

[0164] In formula (18)-(19), t represents the duration of scenario w; w represents the case of maximizing multiple target benefits under one scenario; D represents the case of increasing load; W represents the external demand response part; s represents the case of reducing load; The maximum power provided by the external demand response supplier, represents the maximum power purchased from the demand response trading market; represents the power provided by the external demand response; represents the power purchased from the demand response trading market.

[0165] At the lower level, the distribution system operator schedules the distribution network and clears the day-ahead, with the objective of minimizing its operation cost. Thus, by way of example, the expression for the operation cost minimization is:

[0166] Min∑ w∈Ω π w ∑ t∈T [Cost G +Cost VPP -Income D ]; (20)

[0167]

[0168]

[0169]

[0170] In formula (20)-(23), w represents the case of maximizing multiple target benefits under one scenario; Ω represents multiple target application scenarios; π w represents the occurrence probability of scenario w; t represents the duration of scenario w; T represents the total time of scenario w; Cost G represents the cost paid by the distribution system operator to the generator set; Cost VPP represents the cost paid by the distribution system operator to the virtual power plant; IncomeD represents the revenue obtained by the distribution system operator by selling energy to the load; is the power of the generator set, is the marginal cost of the generator set, a t is the specified price of the virtual power plant; represents the day-ahead settlement power of the virtual power plant; is the incremental power of the external demand response supplier; is the incremental price of the external demand response supplier; G represents the generator set; VPP represents the virtual power plant; D represents the case of increasing load; A represents the day-ahead part.

[0171] Exemplarily, further comprising:

[0172] According to the market inefficiency index, the rate of change of the social welfare of the distribution system operator when the virtual power plant participates or does not participate in the demand response transaction market is quantitatively evaluated.

[0173] As Figure 1 shown, the exemplary embodiments of the present disclosure provide a multi-objective energy management model, comprising: a distribution system operator, a day-ahead transaction market, a virtual power plant, an internal demand response supplier and an external demand response supplier;

[0174] The distribution system operator schedules the distribution network based on minimum operation cost, and settles the day-ahead transaction market to obtain a market settlement result;

[0175] The virtual power plant is used to obtain demand response through the internal demand response supplier and the external demand response supplier based on maximum expected revenue according to the market settlement result.

[0176] Exemplarily, further comprising a demand response transaction market and a regulatory market;

[0177] The internal demand response supplier adjusts the response load according to the bidding signal to provide flexible demand response for the virtual power plant;

[0178] The external demand response supplier provides demand response services to the virtual power plant through the demand response transaction market;

[0179] The virtual power plant makes a decision based on the bid of the internal demand response supplier, the bid of the internal demand response supplier and the price of the regulatory market.

[0180] Exemplarily, the virtual power plant is used to send a call signal to the demand response transaction market to sell excess power or purchase short power according to the result of market settlement;

[0181] The external demand response provider is configured to receive a call signal through the demand response transaction market and provide a demand response service to the virtual power plant according to the received call signal.

[0182] Illustratively, the virtual power plant is configured to sell the excess power at a price higher than the down-regulation price in the demand response transaction market when the total actual output of the virtual power plant exceeds the settlement power in the day-ahead transaction market.

[0183] The virtual power plant is also configured to purchase the short power at a price lower than the up-regulation price in the demand response transaction market when the total actual output power of the virtual power plant is lower than the settlement power in the day-ahead transaction market.

[0184] The above is only a preferred embodiment of the present disclosure, and the protection scope of the present disclosure is not limited to the above-mentioned embodiments. Any technical solutions falling within the concept of the present disclosure shall fall within the protection scope of the present disclosure. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present disclosure shall be considered as falling within the protection scope of the present disclosure.

Claims

1. A multi-objective energy management method, characterized by, The method comprises the following steps: The distribution system operator schedules the distribution network based on operation cost minimization, and clears the day-ahead market to obtain a market clearing result; The virtual power plant obtains demand response from internal demand response providers and external demand response providers based on expected revenue maximization according to the market clearing result; The virtual power plant obtains demand response from internal demand response providers and external demand response providers based on expected revenue maximization according to the market clearing result, which comprises the following steps: The virtual power plant obtains the elastic demand response of the internal demand response providers adjusted according to the bidding signal; The virtual power plant obtains the demand response service of the external demand response providers through a demand response trading market; The virtual power plant makes a decision based on the bid of the external demand response providers, the bid of the internal demand response, and the regulatory market price; The virtual power plant obtains the demand response service of the external demand response providers through a demand response trading market, which comprises the following steps: According to the market clearing result, the virtual power plant sends a calling signal to the demand response trading market to sell excess power or buy short power; The external demand response providers receive the calling signal through the demand response trading market, and provide demand response service to the virtual power plant according to the received calling signal; In the step of receiving the calling signal through the demand response trading market and providing demand response service to the virtual power plant according to the received calling signal, the load provided by the external demand response providers to the demand response trading market is: ；（1） In formula (1), represents the total load that needs to be reduced or increased; represents the option that the external demand response provider can provide the virtual power plant with load reduction, including load reduction, load shifting, and the use of on-site energy storage; represents the option that the external demand response provider can increase the load by adjusting the cost function and benefit; j represents the jth external demand response provider; S represents the case of load reduction; D represents the case of load increase; n represents the total number of external demand response providers.

2. The multi-objective energy management method according to claim 1, wherein, According to the market clearing result, the virtual power plant sends a calling signal to the demand response trading market to sell excess power or buy short power, which comprises the following steps: When the total actual output of the virtual power plant exceeds the settlement power on the day-ahead market, the virtual power plant sells the excess power on the demand response trading market at a price higher than the downward adjustment price; When the total actual output of the virtual power plant is lower than the settlement power on the day-ahead market, the virtual power plant buys short power on the demand response trading market at a price lower than the upward adjustment price.

3. The multi-objective energy management method according to claim 1, wherein When the total actual output of the virtual power plant exceeds the settlement power on the day-ahead market, the external demand response providers increase the load, and the cost function of the bid of the external demand response providers providing demand response service to the virtual power plant is: ；（2） Price of demand response transaction market For: ；（3） or, when the external demand response provider increases its load, increase the bid price of the load is represented as: ；（4） In formulas (2)-(4), represents the price of the demand response transaction market; represents the power to increase the demand response service; is a parameter for price increase; represents the price reduction; X represents the case where the regulatory market reduces the power.

4. The multi-objective energy management method according to claim 1, wherein When the total actual output of the virtual power plant is lower than the settlement power on the day-ahead market, the external demand response providers bid for the utility function of reducing the load on the day-ahead market: ；（5） The bid price of the external demand response supplier is determined by the bidding of the external demand response supplier: ；（6） or, when the external demand response provider j provides a load reduction demand of the bid price of the external demand response provider is : ；（7） In formulas (5) - (7), denotes the bid price of the external demand response provider, denotes the power that the external demand response provider commits to reduce at a bid price of ; is a parameter for the price reduction; denotes the price increase; u denotes the case of an upward adjustment of power on the regulated market.

5. The multi-objective energy management method of claim 1, wherein, The calculation formula of the market inefficiency index MII is: ；（8） In formula (8), SW indicates social welfare; a parameter value for price decrease or increase; a standard value of a parameter value for price decrease or increase.

6. The multi-objective energy management method of claim 1, wherein, The expression of the expected revenue maximization is: ；（9） ；（10） ；（11） ；（12） ； （13） In the formulas (9)-(13), w represents a multi-objective revenue maximization scenario; Ω represents a plurality of target application scenarios; π w represents the occurrence probability of the scenario w; t represents the duration of the scenario w; T denotes the total time of the scenario w lasts; W denotes the external demand response part; A denotes the day-ahead part; N denotes the internal demand response part; M denotes the regulatory market part; n' denotes bus n'; X denotes the regulatory market down power; u denotes the regulatory market up power; denotes the expected profit of the virtual power plant, including the revenue from selling the amount of energy settled on the day-ahead trading market; and denote the revenue from energy trading with the internal demand response providers and the external demand response providers, respectively; denotes the penalty cost of participating in the regulatory market; denotes the day-ahead settled power of the virtual power plant; is the marginal price of bus n'; is the power provided by the internal demand response providers; is the specified price of the internal demand response providers; is the increased power of the external demand response providers; is the increased price of the external demand response providers; is the decreased power of the external demand response providers; is the decreased price of the external demand response providers; is the down power of the regulatory market; is the down price of the regulatory market; is the up power of the regulatory market; is the up price of the regulatory market.

7. The multi-objective energy management method according to claim 6, wherein An increase in price by the external demand response provider For: ； （14） Decreasing price of external demand response providers For: ； （15） Day-ahead settlement power of virtual power plant is: ；（16） in formulas (14) - (16), a parameter representing a price increase; a parameter representing a price decrease; a parameter representing the total rated power of the wind power generator; a parameter representing the power purchased from the demand response trading market; a parameter representing the upward power of the regulatory market; a parameter representing the power provided by the external demand response; The total rated power of the wind power generator is obtained from the wind speed scenario based on the wind power curve by a piecewise linear approximation calculation of the active power of the wind turbine : ；（17） In formula (17), P represents the Pth wind power supplier; 、 、 and respectively represent the wind speed, the rated speed of the wind turbine, the cut-in speed and the cut-out speed under each scenario; represents the rated power of the wind turbine; r represents the rated operating condition. The demand response service purchased or sold by the virtual power plant from the demand response trading market is limited, and is less than the maximum value of the demand response service provided by the external demand response providers: ；（18） ； （19） In formulas (18) - (19), the maximum power provided to the external demand response provider, denotes the maximum power purchased from the demand response trading market.

8. The multi-objective energy management method of claim 1, wherein, The expression of the operation cost minimization is: ；（20） ；（21） ；（22） ；（23） In the formulas (20)-(23), w represents a multi-objective revenue maximization scenario; Ω represents a plurality of target application scenarios; π w represents a probability of occurrence of the scenario w; t represents a duration of the scenario w; T denotes the total time the scenario w lasts; denotes the cost the distribution system operator pays to the generator; denotes the cost the distribution system operator pays to the virtual power plant; denotes the revenue the distribution system operator obtains by selling energy to the load; is the power of the generator, is the marginal cost of the generator, is the specified price of the virtual power plant; denotes the day-ahead settlement power of the virtual power plant; is the increase power of the external demand response provider; is the increase price of the external demand response provider; G denotes the generator; VPP represents a virtual power plant; D represents an increase in load; A represents a day-ahead part.

9. The multi-objective energy management method of claim 5, wherein, Further comprising: According to the market inefficiency index, the rate of change of the social welfare of the distribution system operator when the virtual power plant participates or does not participate in the demand response transaction market is quantitatively evaluated.

10. The multi-objective energy management method of claim 1, wherein The virtual power plant comprises a renewable energy generator set and a plurality of groups of internal demand response devices; The renewable energy generator set comprises a wind turbine and a dispatchable generator set; Each group of internal demand response devices comprises responsive and non-responsive loads, and the internal demand response devices are used to provide demand response services for the virtual power plant.

11. A multi-objective energy management model, characterized by, Comprise: a distribution system operator, a day-ahead transaction market, a virtual power plant, an internal demand response supplier and an external demand response supplier; The distribution system operator schedules the distribution network based on operation cost minimization, and clears the day-ahead transaction market to obtain a market clearing result; The virtual power plant is used to obtain demand response from the internal demand response supplier and the external demand response supplier based on expected revenue maximization according to the market clearing result; Further comprising a demand response transaction market and a regulatory market; The internal demand response supplier adjusts the responsive load according to the bidding signal to provide flexible demand response for the virtual power plant; The external demand response supplier provides demand response services for the virtual power plant through the demand response transaction market; The virtual power plant makes a decision based on the bid of the external demand response supplier, the bid of the internal demand response and the price of the regulatory market; The virtual power plant is used to send a call signal to the demand response transaction market to sell excess power or buy short power according to the result of market clearing; The external demand response supplier is used to receive the call signal through the demand response transaction market, and provide demand response services for the virtual power plant according to the received call signal; In the external demand response supplier receiving the call signal through the demand response transaction market, and providing demand response services for the virtual power plant according to the received call signal, the load amount provided by the external demand response supplier to the demand response transaction market is: ；（1） In formula (1), represents the total load that needs to be reduced or increased; represents the option that the external demand response provider can provide the virtual power plant with the option of reducing load, including reducing load, shifting load, and utilizing on-site energy storage; represents the option that the external demand response provider can increase load by adjusting the cost function and benefit; j represents the jth external demand response provider; S represents the case of reducing load; D represents the case of increasing load; n represents the total number of external demand response providers.

12. The multi-objective energy management model of claim 11, wherein The virtual power plant is used to sell excess power at a price higher than the down-regulation price in the demand response transaction market when the total actual output of the virtual power plant exceeds the settled power in the day-ahead transaction market; Further used to buy short power at a price lower than the up-regulation price in the demand response transaction market when the total actual output of the virtual power plant is lower than the cleared power in the day-ahead transaction market.

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