電力市場環(huán)境下的微電網(wǎng)能量管理系統(tǒng)及風(fēng)險(xiǎn)管理

1、學(xué)號： 姓名：羅任志 專業(yè)：電力系統(tǒng)及其自動(dòng)化 日期：2016年7月A Microgrid Energy Management System and Risk Management under an Electricity Market Environment電力市場環(huán)境下的微電網(wǎng)能量管理系統(tǒng)及風(fēng)險(xiǎn)管理Jingshuang Shen, Chuanwen Jiang, Yangyang Liu and Xu Wang AbstractThis paper presents a novel energy-management method for a microgrid that includes

2、 renewable energy, diesel generators, battery storage, and various loads. We assume that the microgrid takes part in a pool market and responds actively to the electricity price to maximize its profit by scheduling its controllable resources. To address various uncertainties, a risk-constrained scen

3、ario-based stochastic programming framework is proposed using the conditional value at risk method. The designed model is solved by two levels of stochastic optimization methods. One level of optimization is to submit optimal hourly bids to the day-ahead market under the forecast data. The other lev

4、el of optimization is to determine the optimal scheduling using the scenario-based stochastic data of the uncertain resources. The proposed energy management system is not only beneficial for the microgrid and customers but also applies the microgrid aggregator and virtual power plant (VPP). The res

5、ults are shown to prove the validity of the proposed framework. 摘要-本文為微電網(wǎng)提出了一種新的能源管理方法，微電網(wǎng)中包括可再生能源，柴油發(fā)電機(jī)，電池存儲(chǔ)和各種負(fù)載。我們假設(shè)，微電網(wǎng)為合伙經(jīng)營市場中的一部分，并通過調(diào)度其可控的資源積極響應(yīng)的電力價(jià)格，以最大限度地提高利潤。為了解決各種不確定性，提出了一種基于場景的風(fēng)險(xiǎn)約束的隨機(jī)規(guī)劃的框架，用于條件值的風(fēng)險(xiǎn)評估。設(shè)計(jì)的模型是由兩個(gè)層次的隨機(jī)優(yōu)化方法組成。優(yōu)化的一個(gè)方面是提交在預(yù)測數(shù)據(jù)下日前市場每小時(shí)最佳出價(jià)。優(yōu)化另一個(gè)方面是基于不確定資源的場景的隨機(jī)數(shù)據(jù)確定最優(yōu)調(diào)度。本文所提出的能量

6、管理系統(tǒng)不僅有利于微網(wǎng)和客戶也適用于微電網(wǎng)的聚合和虛擬電廠（VPP）。所示結(jié)果證明了提出的框架的有效性。 KeywordsControllable load; Smart grid; Energy management; Electricity market; Microgrid; Renewable energy; Risk management; Stochastic optimization關(guān)鍵詞：可控負(fù)荷；智能電網(wǎng)；能源管理；電力市場；微電網(wǎng)；可再生能源；風(fēng)險(xiǎn)管理；隨機(jī)優(yōu)化I. INTRODUCTIONI. 簡介In recent years, the microgrid has be

7、en growing dramatically due to its potential benefits to provide reliable, secure, efficient, environmentally friendly, and sustainable electricity from renewable energy sources12. A microgrid consists of distributed energy sources, such as micro turbines, wind turbines, fuel cells and photovoltaic

8、system (PVs), storage devices and a group of radial load feeders3. In the grid-connected mode, a microgrid is connected to the grid through a point of common coupling in a low-voltage distribution network. With the development of smart grid technologies, more and more controllable resources in the m

9、icrogrid can exchange information with the higher-level power system45. Hence a microgrid can respond actively to the electricity price to maximize its profit by scheduling its controllable resources. In 6, a price-incentive model was utilized to generate a management strategy to coordinate the char

10、ging of electric vehicles (EVs) and battery swap stations (BSSs) to minimize the total cost of the EVs and maximize the profit from the BSS in grid-connected mode.In 7, the author designed an objective to determine the optimal hourly bids that the microgrid aggregator should submit to the day-ahead

11、market to maximize its profit. In 8, two market bidding techniques, single bidding and discriminatory bidding, were proposed for the microgrid to participate in the bidding process. Much work has been carried out on bidding and auction theory in the competitive electricity market. However, the energ

12、y management and optimal operation for the microgrid under the electricity market environment face challenges.近年來，微電網(wǎng)由于其潛在的好處有了極大的發(fā)展，其能提供可靠，安全，高效，環(huán)保，可再生能源和可持續(xù)的電力能源 1 2 。一個(gè)微電網(wǎng)的分布式能源，如微型燃?xì)廨啓C(jī)、風(fēng)力發(fā)電機(jī)、燃料電池、光伏發(fā)電系統(tǒng)（PVS），存儲(chǔ)設(shè)備和一組徑向載荷饋線 3 。在并網(wǎng)模式下，一個(gè)微電網(wǎng)通過在低壓配電網(wǎng)中的公共耦合點(diǎn)連接到電網(wǎng)。隨著智能電網(wǎng)技術(shù)的發(fā)展， 微電網(wǎng)中越來越多的可控能源可以與高電壓的電力系統(tǒng)進(jìn)

13、行信息交換 4 5 。因此，一個(gè)微電網(wǎng)可以積極響應(yīng)其電力價(jià)格并通過可控資源的調(diào)度，以最大限度地提高其利潤。在 6 ，利用價(jià)格激勵(lì)模型來產(chǎn)生一個(gè)管理策略來協(xié)調(diào)充電的電動(dòng)車（EV）和電池交換站（BSSS），以降低電動(dòng)汽車的總成本和最大限度提高來自在并網(wǎng)中的電池交換站的利潤。在圖 7 中，作者設(shè)計(jì)了一個(gè)服從日前市場利潤最大化的每小時(shí)最佳出價(jià)的目標(biāo)函數(shù)。在 8 中，為微電網(wǎng)的參與競價(jià)提出了兩市場招投標(biāo)技術(shù)、單投標(biāo)和有偏見投標(biāo)技術(shù)。競爭的電力市場中的招標(biāo)和拍賣已經(jīng)完成了許多工作。但是，在電力市場環(huán)境下微電網(wǎng)的能源管理和優(yōu)化運(yùn)行面臨挑戰(zhàn)。As it is an important research fie

14、ld of smart power, several approaches have been reported in the literature in relation to microgrid smart energy management applicable within the smart grid system. In 9, the authors proposed multi-objective intelligent energy management to minimize the operation cost and the environmental impact of

15、 a microgrid. In 10, a novel double-layer coordinated control approach for microgrid energy management was proposed, including a schedule layer obtaining an economic operation scheme based on forecasting data and a dispatch layer providing power to controllable units based on real-time data. In 11,

16、three-level hierarchical energy management strategies were presented for multi-microgrids. Demand response and demand side management have also been considered in the microgrid energy management system 12 13. Overall, most existing works have not roundly considered the uncertainties of elements in t

17、he microgrid system. Renewable energy, such as wind and photovoltaic generation, customer loads and market electricity prices are uncertain and random in real time. Although some works considered the uncertainties of renewable energy 1011, the uncertainties of market electricity prices have seldom b

18、een considered.在許多與適用于智能電網(wǎng)系統(tǒng)的微電網(wǎng)智能能源管理相關(guān)的文獻(xiàn)中已經(jīng)提出，這是智能能源中一個(gè)重要的研究領(lǐng)域。在 9 ，作者提出了多目標(biāo)智能能源管理，以盡量減少微電網(wǎng)的運(yùn)行成本和對環(huán)境的影響。在 10 ，提出了使用一種新型的雙層協(xié)調(diào)控制的方法以達(dá)到微電網(wǎng)能量管理，其中包括一個(gè)基于預(yù)測數(shù)據(jù)獲得經(jīng)濟(jì)運(yùn)行的計(jì)劃層和一個(gè)基于實(shí)時(shí)數(shù)據(jù)對單元進(jìn)行功率可控的調(diào)度層。在 11 ，為多微網(wǎng)提出了三層次的能量管理策略。需求響應(yīng)和需求側(cè)管理也被考慮在微電網(wǎng)能量管理系統(tǒng)中 12 13 ?？傮w而言，大多數(shù)現(xiàn)有的工作沒有全面考慮在微電網(wǎng)系統(tǒng)中的不確定元素?？稍偕茉矗顼L(fēng)和 光伏發(fā)電，客戶負(fù)載和市場

19、電價(jià)是不確定的，實(shí)時(shí)為隨機(jī)的。雖然有些工作可以視為考慮了可再生能源的 10 11 的不確定性，市場電價(jià)的不確定性很少被考慮。In this paper, we present a microgrid energy management system that considers the uncertainties of renewable resources, customer loads and electricity prices. To address various uncertainties, we propose a double-layer scenario-based stoc

20、hastic optimization approach. The first layer obtains an economic operation scheme based on forecasting data, while the second layer provides the power to controllable units based on real-time data. The microgrid schedules the controllable resources to maximize its profit. However, the profit may be

21、 at risk due to the uncertain resources in scenario-based stochastic programs. To constrain the risk, risk management is also proposed in the objective function using the conditional value at risk method.在本文中，我們提出了一種考慮了可再生資源，客戶負(fù)載和電力價(jià)格中的各種不確定性的微電網(wǎng)能源管理系統(tǒng)。為了解決各種不確定性，本文提出了一種基于場景隨機(jī)的雙層優(yōu)化方法。第一層獲得基于預(yù)測數(shù)據(jù)的經(jīng)濟(jì)運(yùn)

22、行方案，而第二層基于實(shí)時(shí)數(shù)據(jù)對功率可控單元進(jìn)行控制。微電網(wǎng)調(diào)度可控的資源以最大限度地提高其利潤。然而，由于基于隨機(jī)場景程序中的不確定資源的存在，利潤是有風(fēng)險(xiǎn)的。為了約束風(fēng)險(xiǎn)，在使用了條件值風(fēng)險(xiǎn)法的目標(biāo)函數(shù)中也提出了風(fēng)險(xiǎn)管理的方法。The remainder of this paper is organized as follows. Section II describes the system model. Section III describes the solution approach. A detailed problem formulation is presented in S

23、ection IV. Several case studies and numerical results are provided in Section V. Finally, Section VI states the concluding remarks and discusses some directions for future works.本文的其余部分內(nèi)容如下。第二節(jié)介紹了系統(tǒng)的模型。第三節(jié)介紹了解決的方案。第四節(jié)描述了一個(gè)詳細(xì)問題的。第五節(jié)闡述了幾個(gè)案例的研究和數(shù)值結(jié)果。最后，第六節(jié)是本文的結(jié)語和討論今后的工作方向。II. MICROGRID COMPONENT MODELI

24、NG II.微電網(wǎng)構(gòu)件建模A.Price ModelingA.價(jià)格模型We assume that a microgrid is connected to the main grid and the grid supplies power to the microgrid to balance the microgrid demand. A two-way communication network is available for a microgrid management center to control the controllable units. We assume that t

25、he microgrid possesses a few diesel generators, storage batteries, wind turbines, PV panels, and controllable loads. The microgrid can procure energy from the wholesale electricity market and can also sell energy back to the market when the local generation is surplus. Under the electric power pool

26、mode, the microgrid is a price-taker. It submits the hourly power quantities that it commits to buy/sell in the day-ahead (DA) energy market to the market operator before the operating day. During the operating day, the microgrid participates in the real-time energy market to compensate for the devi

27、ation from the day-ahead schedule. The market electricity price and quantity are formulated as 我們假設(shè)有一個(gè)微電網(wǎng)連接到主電網(wǎng)，主電網(wǎng)向微電網(wǎng)提供電能以平衡微電網(wǎng)的需求。一個(gè)雙向通信網(wǎng)絡(luò)應(yīng)用于微電網(wǎng)的控制管理中心進(jìn)行控制單元。我們假設(shè)這個(gè)微電網(wǎng)里包含了一些柴油發(fā)電機(jī)，存儲(chǔ)電池，風(fēng)力渦輪機(jī)，光伏電池板，和可控負(fù)載。微電網(wǎng)可以從批發(fā)電力市場購買電能，而產(chǎn)能過剩時(shí)也可以將電能賣回市場。在電力聯(lián)營模式下，微電網(wǎng)是價(jià)格接受方。其每小時(shí)提供的電量為在工作日之前承諾購買或出售給能源市場的數(shù)量。在工作期間，微電網(wǎng)參

28、與到實(shí)時(shí)能源市場中以彌補(bǔ)其錯(cuò)誤前一天的進(jìn)度偏差。市場電價(jià)和數(shù)量的公式如下：whererepresents the market electricity price, and represent the forecast electricity price and forecast error, respectively, represents the electricity quantity that the microgrid buys from the electricity market, and and represent the planning purchase quantity

29、and real-time variation, respectively. The forecast error includes the active variation and passive variation. The active variation represents the variation that the microgrid dispatches actively to maximize its profit when the supply is abundant within the microgrid. The passive variation represent

30、s the variation by which the microgrid must purchase energy from the main grid when the supply is not enough due to forecast error. The mathematical formula is 代表市場電價(jià)，和分別代表預(yù)測電價(jià)和預(yù)測誤差，代表從微電網(wǎng)購買電力市場的電量，和分別表了計(jì)劃采購量和實(shí)時(shí)變化量。預(yù)測誤差包括主動(dòng)變化 和被動(dòng)變化。主動(dòng)變化指的是，當(dāng)微電網(wǎng)調(diào)度積極追求利潤最大化時(shí)對微電網(wǎng)調(diào)度的變化。被動(dòng)變化指的是，微電網(wǎng)由于預(yù)測誤差而供應(yīng)不夠時(shí)必須從主電網(wǎng)中購買電量

31、的變化。數(shù)學(xué)公式是： where and represent the active variation and passive variation, respectively.和分別代表了主動(dòng)變化和被動(dòng)變化。B.Demand Response需求響應(yīng)We assume that the controllable loads are effective controllable units that respond actively to the electricity price. There are two types of controllable loads within the mic

32、rogrid. One type of is passive such as refrigerators, freezers, air conditioners, water heaters and heat pumps, which can be controlled by direct load control (DLC) and interruptible load management (ILM).The other type is active, such as vehicle-to-grid(V2G) and heat storage, which not only can be

33、controlled like the first type but can also supply energy to the main grid. This type can more effectively take part in load management programs. In the electricity market, the controllable loads respond actively to the electricity price as我們假設(shè)，可控負(fù)載是有效的可控單元并能積極響應(yīng)電力價(jià)格。微電網(wǎng)中有兩種類型的可控負(fù)載。一種是被動(dòng)類型例如冰箱、冰柜、空調(diào)

34、、熱水器和熱泵，它們可以通過直接負(fù)荷控制（DLC）和可中斷負(fù)荷管理（ILM）進(jìn)行控制。另一種是主動(dòng)類型，如車輛接入電網(wǎng)（V2G）和儲(chǔ)熱的，它們不僅可以像第一類一樣進(jìn)行控制而且還可以向電網(wǎng)提供能量。這種類型可以更有效地參與負(fù)載管理程序中。在電力市場中，可控負(fù)載可以積極響應(yīng)電價(jià)：Where and represent the electricity price in real time and the reference price, respectively, and represent controllable loads under and respectively, and k repre

35、sents the controllable loads are also stochastic.和分別代表了實(shí)時(shí)電價(jià)和參考電價(jià)，和分別代表在和下的可控負(fù)荷，K 表示可控負(fù)載的隨機(jī)系數(shù)。C.Renewable EnergyC.可再生能源Renewable resource generation such as wind turbines and PV panels is uncertain. For example, the output of wind turbines depends on the wind speed, and the output of PV panels depend

36、s on the irradiance and temperature.可再生資源發(fā)電，如風(fēng)力渦輪機(jī)和光伏板是不確定的。例如，風(fēng)力渦輪機(jī)的輸出取決于風(fēng)速和光伏電池板的輸出取決于輻射和溫度。1)Wind Turbines1)風(fēng)力渦輪機(jī)The output power of the wind turbines is described by :風(fēng)力發(fā)電機(jī)的輸出功率描述為：where and represent the output power and rated output power of the wind turbine, respectively; , , and represent th

37、e wind speed, cut-in speed, rated speed and cut-off speed of the wind turbine, respectively; and a and b are fitting parameters of the wind turbine power curve.和分別代表了風(fēng)力發(fā)電機(jī)的輸出功率和額定輸出功率，、分別代表風(fēng)力發(fā)電機(jī)的風(fēng)速、轉(zhuǎn)速、額定轉(zhuǎn)速和截止轉(zhuǎn)速，a和b是風(fēng)力發(fā)電機(jī)功率曲線的擬合參數(shù)。As shown in Eq. (5), the output of the wind turbines is dependent on t

38、he wind speed, which is obtained by the forecast in the energy-management system, but is unpredictable.如式（5）所示，風(fēng)力發(fā)電機(jī)的輸出依賴于風(fēng)的速度，這是由能源管理系統(tǒng)預(yù)測所得到的，但不是可預(yù)測的。2)PV panels2)光伏電池板The output of a PV generator is a function of the irradiance and temperature, which is provided by a confirmed formula,光伏發(fā)電機(jī)的輸出是輻射和

39、溫度的函數(shù)，它由一個(gè)確定的公式構(gòu)成，where represents the maximum output under standard test conditions; and represent the current irradiance and standard irradiance, respectively; and represent the current temperature and standard temperature, respectively; and k is a temperature coefficient.表示標(biāo)準(zhǔn)測試條件下的最大輸出；和分別代表當(dāng)前輻射和

40、標(biāo)準(zhǔn)輻射，和 分別代表了當(dāng)前溫度和標(biāo)準(zhǔn)溫度，K為一個(gè)溫度系數(shù)。III. MODELING APPROACH 建模的方法A.Stochastic Optimization ApproachA.隨機(jī)優(yōu)化方法In this paper, we propose a two-stage scenario-based stochastic programming approach to address the uncertainties in the microgrid. In the first stage, the forecast data of the uncertainties such as

41、the wind speed, PV power, loads and electricity prices can be obtained by traditional forecasting techniques. Then, a Monte Carlo simulation with the Latin hypercube sampling technique is implemented to generate a large number of scenarios representing values of the uncertain parameters. Forecasting

42、 errors are always present. For simplicity, the forecasting errors of the wind speed, PV power, loads and electricity prices are assumed to follow normal distributions in this paper.在本文中，我們提出了一個(gè)兩階段的基于場景的隨機(jī)規(guī)劃方法，以解決在微電網(wǎng)中存在的不確定性。在第一階段中，不確定性數(shù)據(jù)的預(yù)測，如風(fēng)速、光伏發(fā)電、負(fù)荷和電價(jià)可以由傳統(tǒng)的預(yù)測方法獲得。然后，用帶有Latin hypercube采樣技術(shù)的Mont

43、e Carlo模型實(shí)現(xiàn)生成大量場景的不確定參數(shù)值。預(yù)測錯(cuò)誤總是存在的。為簡單起見，在本文中風(fēng)速，光伏發(fā)電，負(fù)載的預(yù)測誤差和電力價(jià)格被假定為遵循正態(tài)分布的 。It is desirable to generate a large number of scenarios to increase the accuracy of the results. However, the number of generated scenarios directly impacts the computational complexity. To address this trade off, 2000 sce

44、narios are generated using a Monte Carlo simulation with the Latin hypercube sampling(LHS) technique in this paper. A fast-forward reduction method such as The general algebraic modeling system (GAMS)/ scenario reduction (SCENRED) is implemented to reduce the computation time from 2000 scenarios to

45、200 scenarios without affecting the accuracy of the optimization results.它通過產(chǎn)生大量的情況以提高結(jié)果的準(zhǔn)確性，結(jié)果是理想的。然而，所產(chǎn)生的情況直接影響了計(jì)算的復(fù)雜度。為了達(dá)到權(quán)衡，本文使用了帶有Latin hypercube 采樣技術(shù)的Monte Carlo模擬（LHS）生成技術(shù)來產(chǎn)生2000種情況。一種快速的前向還原方法，如在不影響優(yōu)化結(jié)果的準(zhǔn)確性下，用一般代數(shù)建模系統(tǒng)（GAMS）/場景還原（SCENRED）來減少計(jì)算從2000到200種情況下的時(shí)間。B.Risk ManagementB.風(fēng)險(xiǎn)管理As data su

46、ch as the wind speed, PV power, loads and electricity prices are produced randomly in the scenario-based stochastic optimization programming, the profit of the microgrid in the proposed model is indeed uncertain. The optimal expected profit under some scenarios may be very low or even negative. As a

47、 result, the expected profit may be variable and faces a high level of risk. In this paper, we propose a risk management scheme, namely, conditional value at risk (CVaR), to control the trade-off between the expected profit and its variability. 由于如風(fēng)速，光伏發(fā)電，負(fù)載和電力價(jià)格這些數(shù)據(jù)在基于場景的隨機(jī)優(yōu)化規(guī)劃中是隨機(jī)產(chǎn)生的，所提出的微電網(wǎng)模型的利潤的

48、確是不確定的。在某些情況下，最佳的預(yù)期利潤可能是非常低的，甚至是負(fù)的。因此，預(yù)期的利潤可能是可變的，并面臨著高水平的風(fēng)險(xiǎn)。在本文中， 我們提出了一個(gè)風(fēng)險(xiǎn)管理方案，即條件風(fēng)險(xiǎn)價(jià)值（CVaR），用以控制預(yù)期利潤與其變化之間的權(quán)衡。Several risk measures have been introduced to quantify risk 14, and one of the most popular is Value-at-Risk (VaR). However, it has undesirable mathematical characteristics such as a lack

49、 of subadditivity and convexity. VaR is also difficult to optimize when it is calculated from scenarios. As an alternative measure of risk, CVaR is known to have advantages over VaR in that it is transition-equivariant, positively homogeneous, convex, and has a stochastic dominance of order 1. In th

50、e scenario-based stochastic optimization method, the conditional value at risk at the confidence level (-CVaR) can be defined as the expected profit in the (1-)100% worst scenarios,which is expressed as 14幾個(gè)風(fēng)險(xiǎn)措施已被引入到量化風(fēng)險(xiǎn)中 14 ，其中最受歡迎的是風(fēng)險(xiǎn)價(jià)值（風(fēng)險(xiǎn)值）。然而，它有不良的數(shù)學(xué)特征，例如缺乏可加性和凸性。當(dāng)從多個(gè)場景來計(jì)算是是風(fēng)險(xiǎn)值也是難以優(yōu)化的。作為風(fēng)險(xiǎn)的替代計(jì)量，

51、CVaR對于VAR具有過渡等變，正齊次，凹凸有致，1階隨機(jī)主導(dǎo)地位的優(yōu)點(diǎn)。在基于場景的隨機(jī)優(yōu)化方法中，風(fēng)險(xiǎn)條件值的置信水平（CVaR）可以被定義為在在（1 -）100%最壞的情況的預(yù)期利潤，可以表示為式 14 Where re presents a random variable and represents a confidence level.re表示一個(gè)隨機(jī)變量，表示置信水平。In 7, the author supplies a CVaR solving method, which is represented as ：在 7 ，作者提出了CVaR的求解方法，可以表示為：where N

52、S represents the number of Monte Carlo scenarios; s represents the probability of scenario s, represents the VaR, and represents an auxiliary nonnegative variable equal to the difference between the VaR and profits is smaller than VaR and equal to zero otherwise.其中n代表Monte Carlo情況的數(shù)量；代表某種情況的概率，代表無功，

53、和表示一個(gè)輔助的非負(fù)變量等于VaR和利潤之間的差大于VaR的體積更小，等于否則為0。IV. P ROBLEM F ORMULATIONIV.問題描述In this paper, we propose a double-level stochastic optimization method to maximize the profit of the microgrid. We assume that the microgrid always operates in grid-connected mode. The objective of the model is to maximize th

54、e profit of the microgrid over a given time period together with achieving risk management. Normally, the operation costs of renewable energy and energy storage such as batteries are minimal. Therefore, renewable energy and battery operation costs are not considered in this paper. The objective func

55、tion is given as在本文中，我們提出了一個(gè)雙層隨機(jī)優(yōu)化方法，以最大限度地提高微電網(wǎng)的利潤。我們假設(shè)，微電網(wǎng)總是運(yùn)行在連接電網(wǎng)的模式。該模型的目的是給定的時(shí)間段實(shí)現(xiàn)微電網(wǎng)的風(fēng)險(xiǎn)管理使利潤最大化。通常情況下，可再生能源和能源存儲(chǔ)運(yùn)行成本的是最小的，例如電池。因此，本文不考慮可再生能源和電池的運(yùn)行成本。目標(biāo)函數(shù)可以表示為：where N S represents the number of Monte Carlo scenarios; s represents the probability of scenario s; profits represents the profit of

56、 the microgrid in scenario s; and represents the risk aversion parameter. When is equal to zero, the microgrid is a risk-neutral decision maker. With increasing, the microgrid becomes more risk-averse其中n代表Monte Carlo情況的數(shù)量；代表情況的概率； s代表微電網(wǎng)在某種情況下的利潤；代表風(fēng)險(xiǎn)規(guī)避參數(shù).當(dāng)?shù)扔诹?，微網(wǎng)是一個(gè)風(fēng)險(xiǎn)中性的決定者。隨著增加，微網(wǎng)變得越來越規(guī)避風(fēng)險(xiǎn)。The prof

57、it of the microgrid in scenarios is在某種情況下微電網(wǎng)的利潤是：where represents the profit of the microgrid obtained from the main grid; represents the cost of the distributed generation (DG) in the microgrid; and represent the electrical energy sold and bought from the grid tie line in scenario s, respectively;

58、and represent the sell and buy electricity prices in scenario s, respectively; and represent the penalty amount and penalty price when the actual power is not equal to the plan exchange power in the tie line and scenario s;represents the i th generator cost of fuel; represents the i th generator maintenance cost; represents the total number of diesel generators; represents the i generator power in period t and scenario s; represents the i generator start-up cost; and represents the i generator status in period t and sc