毕业论文-电力市场环境下的微电网能量管理系统及风险管理

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1、学号： 姓名： 专业：电力系统及其自动化 日期： 2016 年 7 月 A Microgrid Energy Management System and Risk Management under an Electricity Market Environment 电力市场环境下的微电网能量管理系统及风险管理 Jingshuang Shen, Chuanwen Jiang, Yangyang Liu and Xu Wang Abstract This paper presents a novel energy-management method for a microgrid that inc

2、ludes 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

3、 scenario-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 othe

4、r level 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). Th

5、e results are shown to prove the validity of the proposed framework. 摘要 -本文为微电网提出了一种新的能源管理方法，微电网中包括可再生能源，柴油发电机，电池存储和各种负载。我们假设，微电网为合伙经营市场中的一部分，并通过调度其可控的资源积极响应的电力价格，以最大限度地提高利润。为了解决各种不确定性，提出了一种基于场景的风险约束的随机规划的框架，用于条件值的风险评估。设计的模型是由两个层次的随机优化方法组成。优化的一个方面是提交在预测数据下日前市场每小时最佳出价。优化另一个方面是基于不确定资源的场景的随机数据确定最优调度。本文

6、所提出的能量 管理系统不仅有利于微网和客户也适用于微电网的聚合和虚拟电厂（ VPP）。所示结果证明了提出的框架的有效性。 Keywords Controllable load; Smart grid; Energy management; Electricity market; Microgrid; Renewable energy; Risk management; Stochastic optimization 关键词：可控负荷；智能电网；能源管理；电力市场；微电网；可再生能源；风险管理；随机优化 I. INTRODUCTION I. 简介 In recent years, the mic

7、rogrid has been 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

8、photovoltaic 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 resou

9、rces in the microgrid 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 coordi

10、nate the charging 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 t

11、he day-ahead 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. Howev

12、er, the energy management and optimal operation for the microgrid under the electricity market environment face challenges. 近年来，微电网由于其潜在的好处有了极大的发展，其能提供可靠，安全，高效，环保，可再生能源和可持续的电力能源 1 2 。一个微电网的分布式能源，如微型燃气轮机、风力发电机、燃料电池、光伏发电系统（ PVS） ，存储设备和一组径向载荷馈线 3 。在并网模式下，一个微电网通过在低压配电网中的公共耦合点连接到电网。随着智能电网技术的发展， 微电网中越来越多的

13、可控能源可以与高电压的电力系统进行信息交换 4 5 。因此，一个微电网可以积极响应其电力价格并通过可控资源的调度，以最大限度地提高其利润。在 6 ，利用价格激励模型来产生一个管理策略来协调充电的电动车（ EV）和电池交换站（ BSSS），以降低电动汽车的总成本和最大限度提高来自在并网中的电池交换站的利润。在图 7 中，作者设计了一个服从日前市 场利润最大化的每小时最佳出价的目标函数。在 8 中，为微电网的参与竞价提出了两市场招投标技术、单投标和有偏见投标技术。竞争的电力市场中的招标和拍卖已经完成了许多工作。但是，在电力市场环境下微电网的能源管理和优化运行面临挑战。 As it is an im

14、portant research field 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 env

15、ironmental impact of 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 rea

16、l-time data. In 11, 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 uncertaint

17、ies of elements in the 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

18、prices have seldom been considered. 在许多与适用于智能电网系统的微电网智能能源管理相关的文献中已经提出，这是智能能源中一个重要的研究领域。在 9 ，作者提出了多目标智能能源管理，以尽量减少微电网的运行成本和对环境的影响。在 10 ，提出了使用一种新型的双层协调控制的方法以达到微电网能量管理，其中包括一个基于预测数据获得经济运行的计划层和一个 基于实时数据对单元进行功率可控的调度层。在 11 ，为多微网提出了三层次的能量管理策略。需求响应和需求侧管理也被考虑在微电网能量管理系统中 12 13 。总体而言，大多数现有的工作没有全面考虑在微电网系统中的不确定元素。

19、可再生能源，如风和 光伏发电，客户负载和市场电价是不确定的，实时为随机的。虽然有些工作可以视为考虑了可再生能源的 10 11 的不确定性，市场电价的不确定性很少被考虑。 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-la

20、yer scenario-based stochastic 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. How

21、ever, the profit may be 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，我们提出了一种考虑了可再生资源，客户负载和电力价格中的各种不确定性的微电网能源管理系统。为了解决各种不确定性，本文提出了一种基于场景随

22、 机的双层优化方法。第一层获得基于预测数据的经济运行方案，而第二层基于实时数据对功率可控单元进行控制。微电网调度可控的资源以最大限度地提高其利润。然而，由于基于随机场景程序中的不确定资源的存在，利润是有风险的。为了约束风险，在使用了条件值风险法的目标函数中也提出了风险管理的方法。 The remainder of this paper is organized as follows. Section II describes the system model. Section III describes the solution approach. A detailed problem for

23、mulation is presented in Section 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. 本文的其余部分内容如下。第二节介绍了系统的模型。第三节介绍了解决的方案。第四节描述了一个详细问题的。第五节阐述了几个案例的研究和数值结果。最后，第六节是本文的结语和讨论今后的工作方向。 II

24、. MICROGRID COMPONENT MODELING II.微电网 构件建模 A.Price Modeling A.价格模型 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 cont

25、rollable units. We assume that the 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

26、. Under the electric power pool 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 m

27、arket to compensate for the deviation from the day-ahead schedule. The market electricity price and quantity are formulated as 我们假设有一个微电网连接到主电网，主电网向微电网提供电能以平衡微电网的需求。一个双向通信网络应用于微电网的控制管理中心进行控制单元。我们假设这个微电网里包含了一些柴油发电机，存储电池，风力涡轮机，光伏电池板，和可控负载。微电网可以从批发电力市场购买电能，而产能过剩时也可以将电能卖回市场。在电力联营模式下，微电网是 价格接受方。其每小时提供的电量

28、为在工作日之前承诺购买或出售给能源市场的数量。在工作期间，微电网参与到实时能源市场中以弥补其错误前一天的进度偏差。市场电价和数量的公式如下： where tB,pr represents the market electricity price, and t,prBP represent the forecast electricity price and forecast error, respectively, t,BQ represents the electricity quantity that the microgrid buys from the electricity mark

29、et, and t,BFQ and t,BFQ represent the planning purchase quantity 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

30、is abundant within the microgrid. The passive variation represents 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 tB,pr 代表市场电价， tBP,pr 和 t,prBP 分别代表预测电价和预测误差， t,BQ 代表从微电网购买电力市场的电量， t,BFQ 和

31、t,BFQ 分别表了计划采购量和实时变化量。预测误差包括主动变化 和被动变化。主动变化指的是，当微电网调度积极追求利润最大化时对微电网调度的变化。被动变化指的是，微电网由于预测误差而供应不够时必须从主电网中购买电量的变化。数学公式是： where tBP,pr and t,prBAC represent the active variation and passive variation, respectively. tBP,pr 和 t,prBAC 分别代表了主动变化和被动变化。 B.Demand Response 需求响应 We assume that the controllable l

32、oads are effective controllable units that respond actively to the electricity price. There are two types of controllable loads within the microgrid. One type of is passive such as refrigerators, freezers, air conditioners, water heaters and heat pumps, which can be controlled by direct load control

33、 (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 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 elect

34、ricity market, the controllable loads respond actively to the electricity price as 我们假设，可控负载是有效的可控单元并能积极响应电力价格。微电网中有两种类型的可控负载。一种是被动类型例如冰箱、冰柜、空调、热水器和热泵，它们可以通过直接负荷控制（ DLC）和可中断负荷管理（ ILM）进行控制。另一种是 主动类型，如车辆接入电网（ V2G）和储热的，它们不仅可以像第一类一样进行控制而且还可以向电网提供能量。这种类型可以更有效地参与负载管理程序中。在电力市场中，可控负载可以积极响应电价： t,prBPWhere tB

35、,pr and tpr，B represent the electricity price in real time and the reference price, respectively, ,tLDP and t,LDP represent controllable loads under tB,pr and tpr，B respectively, and k represents the controllable loads are also stochastic. tB,pr 和 tpr，B 分别代表了实时电价和参考电价， ,tLDP 和 t,LDP 分别代表在 tB,pr 和 tp

36、r，B 下的可控负荷， K 表示可控负载的随机系数。 C.Renewable Energy C.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 depends on the irradiance and temperature. 可再生资源发电，如风力涡轮机和光伏板是不确定的。例如，

37、风力涡轮机的输出取决于风速和光伏电池板的输出取决于辐射和温度。 1)Wind Turbines 1)风力涡轮机 The output power of the wind turbines is described by : 风力发电机的输出功率描述为： where WTP and rateP represent the output power and rated output power of the wind turbine, respectively; V , ciV , rV and coV represent the wind speed, cut-in speed, rated s

38、peed and cut-off speed of the wind turbine, respectively; and a and b are fitting parameters of the wind turbine power curve. WTP 和 rateP 分别代表了风力发电机的输出功率和额定输出功率， V 、 ciV 、 rV 、 coV 分别代表风力发电机的风速、转速、额定转速和截止转速， a 和 b 是风力发电机功率曲线的拟合参数。 As shown in Eq. (5), the output of the wind turbines is dependent on

39、the wind speed, which is obtained by the forecast in the energy-management system, but is unpredictable. 如式（ 5）所示，风力发电机的输出依赖于风的速度，这是由能源管理系统预测所得到的，但不是可预测的。 2)PV panels 2)光伏电池板 The output of a PV generator is a function of the irradiance and temperature, which is provided by a confirmed formula, 光伏发电机

40、的输出是辐射和温度的函数，它由一个确定的公式构成， where max,STCP represents the maximum output under standard test conditions; ACG and STCG represent the current irradiance and standard irradiance, respectively; eT and STCT represent the current temperature and standard temperature, respectively; and k is a temperature coe

41、fficient. max,STCP 表示标准测试条件下的最大输出； ACG 和 STCG 分别代表当前辐射和标准辐射， eT 和 STCT 分别代表了当前温度和标准温度， K 为一个温度系数。 III. MODELING APPROACH 建模的方法 A.Stochastic Optimization Approach A.随机优化方法 In this paper, we propose a two-stage scenario-based stochastic programming approach to address the uncertainties in the microgri

42、d. In the first stage, the forecast data of the uncertainties such as 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

43、scenarios representing values of the uncertain parameters. Forecasting 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，我们提出了一个两阶段的基于场景的随机规划方法，以解决在微电网中存在的不确定性。在第一

44、阶段中，不确定性数据的预测，如风速、光伏发电、负荷和电价可以由传统的预测方法获得。然后，用带有 Latin hypercube 采样技术的 Monte Carlo 模型实现生成大量场景的不确定参数值。预测错误总是存在的。为简单起见，在本文中风速，光伏发电，负载的预测误差和电力价格被假定为遵循正态分布的 。 It is desirable to generate a large number of scenarios to increase the accuracy of the results. However, the number of generated scenarios direct

45、ly impacts the computational complexity. To address this trade off, 2000 scenarios 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 (SC

46、ENRED) is implemented to reduce the computation time from 2000 scenarios to 200 scenarios without affecting the accuracy of the optimization results. 它通过产生大量的情况以提高结果的准确性，结果是理想的。然而，所产生的情况直接影响了计算的复杂度。为了达到权衡，本文使用了带有 Latin hypercube 采样技术的 Monte Carlo 模拟（ LHS）生成技术来产生 2000 种情况。一种快速的前向还原方法，如在不影响优化结果的准确性下，用

47、一般代数建模系统（ GAMS） /场景还原（ SCENRED）来减少计算从 2000到 200 种情况下的时间。 B.Risk Management B.风险 管理 As data such 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 uncer

48、tain. The optimal expected profit under some scenarios may be very low or even negative. As a 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

49、 expected profit and its variability. 由于如风速，光伏发电，负载和电力价格这些数据在基于场景的随机优化规划中是随机产生的，所提出的微电网模型的利润的确是不确定的。在某些情况下，最佳的预期利润可能是非常低的，甚至是负的。因此，预期的利润可能是可变的，并面临着高水平的风险。在本文中， 我们提出了一个风险管理方案，即条件风险价值（ CVaR），用以控制预期利润与其变化之间的权衡。 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 of subadditivity and convexity. VaR is also difficult to optimize when it is calculated from scenar