考虑需求响应激励机制的园区综合能源系统博弈优化调度

建设源荷协调、灵活互动的综合能源系统是构建新型电力系统的有效路径.在综合能源系统下,通过价格、补贴等激励手段合理调节用户侧需求响应机制可以促进综合能源系统的经济高效运行.为制定合理的需求响应激励机制,首先充分考虑源侧可再生能源出力和负荷侧多类能源需求的不确定性,提出随机场景生成策略;然后提出计及用户需求响应的综合能源系统博弈优化框架,分别以园区综合能源系统运营商和综合能源用户效益最大化为目标,建立双主体博弈优化调度模型,并提出快速高效的求解算法;最后,基于某实际园区综合能源系统开展多场景算例仿真分析,制定合理有效的需求响应价格激励方案.调度结果表明,所提出方案可以有效提升综合能源系统运行商和用户的效益.     

多区域互联综合能源系统的双层博弈优化运行

为保障用户日益增长的能源消费需求、提高能源利用率,本文面向多区域综合能源系统,考虑综合需求响应,提出基于聚合商和用户双层博弈的优化运行方法.在包含聚合商、用户、综合能源系统和能源互联网络的多园区综合能源系统架构的基础上,建立了包括下层用户间非合作博弈和上层聚合商联盟合作博弈的双层博弈优化模型.其中,上层聚合商以多区域综...     

先进绝热压缩空气储能电站日前电力市场主从博弈竞标策略

先进绝热压缩空气储能(advanced adiabatic compressed air energy storage,AA–CAES)是支撑电力系统削峰填谷、阻塞管理及可再生能源消纳的有效手段之一.在储能产业商业化初期,研究面向日前电力市场的竞标策略对实现AA–CAES电站的经济运行大有裨益.本文在计及压力动态的AA–CAES运行模型基础上,采用主从博弈研究了AA–CAES电站竞标策略.作为主从博弈Leader,AA–CAES电站运营商向日前电力市场上报竞标标的(储能功率、发电功率、储能电价及发电电价);作为主从博弈Follower,市场交易机构以最大化社会福利出清电力市场.为高效求解主从博弈竞标模型,采用Karush-Kuhn-Tucker(KKT)最优性条件及布尔展开法将双层主从博弈竞标模型转化为单层混合整数线性规划.IEEE–24节点测试系统算例表明,AA–CAES电站可利用低谷电进行充电,在高峰时刻再以高价售出,实现套利运行.基于主从博弈的日前电力市场竞标策略,可支撑AA–CAES电站的经济运行.     

考虑新能源消纳的综合能源系统多层博弈策略

研究了综合能源系统多市场主体参与新能源消纳的博弈行为分析问题。建立了从综合能源系统电、气、热供给到终端用户电、热、冷需求的多能流供应、转换、存储、消费的多级竞标策略优化模型,提出了涉及多能源趸售-零售-终端环节的Cournot-Stackelberg两层主从博弈结构;提出了相应的迭代求解算法,并在定价机制、多能互补、储能配置等方面对博弈均衡结果进行了算例分析。     

基于收益率门槛限制考虑的网格资源拍卖问题

基于收益率门槛限制的视角,通过建立效用函数模型并结合动态博弈理论,对网格资源的拍卖问题进行了探讨.在对网格资源提供者与竞标网格资源使用者的动态博弈过程进行分析时发现,网格资源提供者的最优策略选择决定于其对货币收益与非货币收益的偏好程度,以及网格资源使用者的最高报价.在收益率门槛给定的条件下,当参与竞标的网格资源使用者具有较低的生产利润或付出较高的努力成本时,网格资源使用者将会选择价格较低的投标策略.研究结果表明收益率门槛机制的引入,在一定程度上可以使得参与双方的效用达到最大化.     

基于多主体博弈的综合能源系统运行优化方法

30·60双碳目标的实现,要求可再生能源的大规模消纳,因此构建可大幅就地消纳可再生能源的综合能源系统具有重要意义.在此背景下,本文提出了一种基于合作博弈的综合能源系统运行优化方法,能够有效降低系统用能成本和碳排放,激励不同主体参与系统整体协调优化运行.本文在考虑系统运行成本和二氧化碳排放量的基础上,建立了综合能源系统合...     

基于竞标模式和访问热度的副本创建策略

研究典型数据副本创建策略,提出一种以节点在线率和节点存储能力作为价格的竞标模型副本创建策略。根据对基于访问热度的典型副本创建策略和基于竞标模型的典型副本创建策略的具体分析,改进典型副本创建策略对节点性能考虑的单一性,综合考虑节点的访问热度和负载等性能,实现基于竞标模式和访问热度相结合的副本创建策略。并通过基于网络带宽利用率和系统响应时间的实验,将该副本创建策略与两种典型副本创建策略进行实验对比,验证提出的基于竞标模式和访问热度的副本创建策略具有较好的性能。     

基于供需博弈的园区综合能源微网系统优化

为了提高综合能源系统性能，采用DeST软件对某办公园区(包含居住建筑、办公建筑、商业建筑)的冷热电负荷进行全年逐时动态模拟分析，并建立了3种综合能源系统的数学模型。提出了基于经济-环境效益的评价指标，并基于供需博弈对综合能源微网系统进行了配置优化，确定了不同的成本价格参数对优选方案的影响。环境效益评估结果表明：燃气轮机+光伏+风电系统的综合效益最佳，净现值成本较燃气轮机+光伏、燃气轮机+风电分别降低9.93%和3.24%;同时，该系统的二氧化碳排放量最低，碳排放量较传统分产系统降低约60%。该研究结果可为综合能源微网系统容量配置及运行优化提供参考。     

考虑碳排放和综合需求响应的电-气联合运行决策博弈

考虑到当前向国际社会承诺的“碳达峰,碳中和”目标,针对电-气联合运行中多方利益的诉求,运用动态主从博弈理论,在考虑碳排放和综合需求响应的情况下,建立以供电公司为主体,家庭负荷聚合集群为从体的博弈模型.通过家庭负荷聚合的多能联合负荷特性和价格需求响应的不确定性,得到博弈双方支付函数.考虑能源结构的碳排放折算,以供能方收益最大,需求侧支付费用最低为目标,建立主从博弈模型.主方以价格为策略集,从方以需求响应为策略集,通过最优反应函数结合非支配排序遗传算法求解并筛选主从博弈均衡解.通过算例仿真验证,所提出模型可实现主体和从体各方的社会效益与经济效益最优化,为能源互联下的市场决策优化运行提供参考.     

基于双层主从博弈的多微电网能源管理方法

提出一种双层主从博弈的多微电网的能源管理方法.上层博弈中,多个微电网间根据自身能源剩余量构成能源买卖博弈的领导一方,领导方通过调整售电价格来使自身利益最大化,跟随者一方则通过调整用能策略来降低用能支出;下层博弈中,卖方微电网直接向用户售电,买方微电网则和用户展开博弈,从而达到彼此利益最大化.通过求解博弈模型,得到每个微...     

基于区块链的电力交易模型及博弈定价方法

为解决传统能源中心化交易模式缺少灵活性、透明性和可监督性等问题,满足新型电力系统中电网各主体间电力交易市场化、定价灵活化等新要求,提出一种基于区块链的电力交易模型及博弈定价方法。该电力交易模型具有分布式、去中心化、不可篡改和加密安全等优势。首先,建立基于区块链的电力交易模型,协调发、供、用等主体间的生产和消费行为,形成统一的市场机制;其次,提出了基于博弈的多时间尺度电力交易竞价机制,利用蚁群优化(ACO)算法求解可得每小时的最佳竞拍价格。最后,通过仿真验证了交易模型及博弈定价方法,在激励政策下各交易方收益最优化。结果表明,交易模型及定价方法在新能源参与交易背景下能有效地平衡市场各主体的效益;基于区块链的智能合约可实现电力交易过程的智能化、透明化和可追溯性。     

Maximizing sellers’ welfare in online auction by simulating bidders’ proxy bidding agents

This paper presents an agent-based simulation model to estimate the effects of auction parameters on the auction outcomes, and to find an optimal or, at least, a near-optimal infrastructure. In particular, this study intends to study how bidders’ personalities and bidding strategies with other auction parameters affect the closing prices in two auction mechanisms: English and Yankee auction. Experimental results show that the aggregated outcome of multiple English auctions is more favorable to auctioneers and sellers than that of a corresponding single Yankee auction. It is also shown that raising minimum bid increment or bid starting price positively affects the closing prices in both auction types. However, two auction systems respond differently to the changes in parameter values in terms of magnitude and robustness. Further, it is noted that the closing price of an auction is negatively related to the proportion of risk-takers who adopt sniping strategy to submit their bids. As the proportion of risk-takers increases, the closing price significantly decreases in hard-ending auctions (eBay-like system), but only marginally in soft-ending auctions (Amazon-like system). In particular, soft-ending auctions with less snipers and a longer time window of automatic extension return the most favorable closing price to sellers.     

考虑节点边际价格的热电联产机组主从博弈竞价策略

本文构建了以热电联产机组(combined heat and power unit,CHP)、电力市场和热力市场为参与者的主从博弈模型,并基于电力市场中节点边际电价(locational marginal electricity price,LMEP)的概念,提出了节点边际热价(locational marginal heat price,LMHP)的概念.在节点边际电价的求解中,采用了支路潮流(branch power flow,BPF)模型,考虑了配电网中的网络损耗从而可以得到更精确的计算结果.在节点边际热价的求解中,考虑了管道热损耗,并基于管道损耗方程分析了节点边际热价的分布规律.在此基础上,采用变步长迭代寻优算法求解热电联产机组、电力市场、热力市场各自最优出力和最优报价策略.最后,通过一个6节点电网–4节点热网的算例对所构建的主从博弈模型及热电联产机组的竞价策略进行了验证.     

Auction-based resource allocation game under a hierarchical structure

This paper studies a class of auction-based resource allocation games under a hierarchical structure, such that each supplier is assigned a certain amount of resource from a single provider and allocates it to its buyers with auction mechanisms. To implement the efficient allocations for the underlying hierarchical system, we first design an auction mechanism, for each local system composed of a supplier and its buyers, which inherits the advantages of the progressive second price mechanism. By employing a dynamic algorithm, each local system converges to its own efficient Nash equilibrium, at which the efficient resource allocation is achieved and the bidding prices of all the buyers in this local system are identical with each other. After the local systems reach their own equilibria respectively, the resources assigned to suppliers are readjusted via a dynamic hierarchical algorithm with respect to the bidding prices associated with the implemented equilibria of local systems. By applying the proposed hierarchical process, the formulated hierarchical system can converge to the efficient allocation under certain mild conditions. The developed results in this work are demonstrated with simulations.     

资源有限连续双向拍卖环境下的理性策略

网络系统的动态资源分配是未来IT系统必须解决的一个基本问题。针对agent资源的有限性,提出了连续双向拍卖环境下（Continuous Double Auction,CDA）agent具有理性行为的GD2策略。GD2策略是一种包含价格和数量的二维报价策略,agent通过建立信任函数和计算最大期望利润调整报价,实验表明GD2策略可以实现较高的动态资源分配效率,平均分配效率超过98%。     

A soft computing system for day-ahead electricity price forecasting

Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM–SVM–PSO has considerable value in forecasting day-ahead price in Pennsylvania–New Jersey–Maryland (PJM) market, especially for summer peak prices.     

A new feature selection algorithm and composite neural network for electricity price forecasting

In a competitive electricity market, the forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. In this paper, a new forecast strategy is proposed for day ahead prediction of electricity price, which is a complex signal with nonlinear, volatile and time dependent behavior. Our forecast strategy includes a new two stage feature selection algorithm, a composite neural network (CNN) and a few auxiliary predictors. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information (MI) criterion and selects the input variables of the CNN among a large set of candidate inputs. The CNN is composed of a few neural networks (NN) with a new data flow among its building blocks. The CNN is the forecast engine of the proposed strategy. A kind of cross-validation technique is also presented to fine-tune the adjustable parameters of the feature selection algorithm and CNN. Moreover, the proposed price forecast strategy is equipped with a few auxiliary predictors to enrich the candidate set of inputs of the forecast engine. The whole proposed strategy is examined on the PJM, Spanish and Californian electricity markets and compared with some of the most recent price forecast methods.     

Optimal reserve prices in weighted GSP auctions

Most search engines use the weighted Generalized Second Price (wGSP) auction to sell keyword-based text ads, generating billions of dollars of advertising revenue every year. Designing and implementing near-optimal reserve prices for these wGSP auctions are naturally important problems for both academia and industry.In this paper, we show how to calculate and implement the near-optimal reserve price of the wGSP mechanism in realistic settings. Unlike reserve prices in standard single-item auctions, optimal reserve prices in wGSP auctions are discriminatory, different even for advertisers bidding on the same keyword. The optimal reserve price results can be extended to support CPA/CPC/CPM¹ hybrid auctions.Our simulations indicate that setting a proper reserve price will transfer some bidder utility (payoff) to auctioneer utility, resulting in higher revenue for the search engine. We describe a practical methodology to implement optimal reserve prices in production systems.     

Interplay between Buy-It-Now price and last minute bidding on online bidding strategies

Sellers and buyers on online auction sites like eBay have the option of setting and executing auction parameters such as auction length, Buy-It-Now price, starting price, reserve price, etc. Understanding why bidders choose to execute the Buy-It-Now price as opposed to submitting a bid at the last minute of an auction helps managers better choose auction parameters and generate more revenue. In this paper, we first study online bidder behavior pertaining to the execution of the Buy-It-Now option as opposed to the last minute bidding strategy on eBay. Consequently, we study the seller’s decision to set a Buy-It-Now option and the amount of it. Our main finding is that a dominant strategy for the seller is to offer a Buy-It-Now option at the beginning of the auction. An early bidder arriving at the auction site is indifferent in choosing threshold Buy-It-Now prices or moving onto the auction and under particular circumstances strategically waiting for the last minutes of the auction to submit a bid. We also provide conditions on the existence of a set of equilibria which predicts the outcome of the game of executing the Buy-It-Now option versus last-minute bidding on eBay.     

An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter-varying MPC computes the optimal trajectory in closed-loop by iteratively linearizing the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon, while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.