A method for power suppliers’optimal cooperative bidding strategies considering network losses

The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.

Bidding Strategy in Deregulated Power Market Using Differential Evolution Algorithm

The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).

Wind Power Bidding Strategy Based on the Minimax Regret Criterion with Limited Distribution Information

In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power output is known. In real world applications, however, only very limited distribution information can be obtained. Therefore, the “optimal bidding strategy” obtained based on the hypothetical distribution may be far away from the true optimal one. In this paper, an optimal bidding strategy is obtained based on the minimax regret criterion. The salient feature of the new approach is that it requires only partial information of wind power distribution, for example, the expectation and the support set. Numerical test is then performed and the results suggest that the method established in this paper is effective.

Bivariate Stochastic Optimization Model for Bidding Strategies Considering Competition Among Renewable Power Producers

The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.

Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits

Photovoltaic(PV)and battery energy storage systems(BESSs)are key components in the energy market and crucial contributors to carbon emission reduction targets.These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading.This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets,with a specific focus on carbon reduction benefits.A two-stage bidding framework that optimizes the profit of PV and BESSs is presented.In the first stage,the day-ahead energy market takes into account potential real-time forecast deviations.In the second stage,the real-time balancing market uses a rolling optimization method to account for multiple uncertainties.Notably,a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control(AGC).This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market.This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error(ACE)occurs.The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

FAST HUGS BID策略在ICU低年资护士培训中的应用

目的:探讨FAST HUGS BID策略在ICU低年资护士培训中的应用效果。方法:选取2019年7月1日~12月31日ICU低年资护士79名作为研究对象,采用随机数字表法分为实验组39名和对照组40名。对照组采用医院ICU护士常规培训,实验组在ICU护士常规培训基础上接受基于FAST HUGS BID策略制订的培训课程。比较两组护士核心能力评分情况、ICU护理不良事件发生情况、综合考核成绩、培训效果满意度。结果:实施后,实验组临床护理、专业发展、评判性思维与科研能力、综合考核成绩评分均优于对照组(P<0.05,P<0.01);实验组非计划拔管发生率低于对照组(P<0.05);实验组授课质量、掌握程度、学习收获评分及总分均高于对照组(P<0.01)。结论:FAST HUGS BID策略的应用有助于提升ICU低年资护士临床思维能力、重症专业技能,有效降低非计划拔管的发生率,提升培训满意度。

Commission Strategy of the Auction House

In a SIPV model, when the commission proportion is not certain, but related with bargain price, generally, it is a linear function of the bargain price, this paper gives bidders＇ equilibrium bidding strategies in the first-and secondprice auctions. We find that the equilibrium strategies in second-price auction are dominant strategies. For seller or auction house, whether the fixed proportion or the unfixed proportion is good is not only related with constant item and the linear coefficient of the linear function, the size of the fixed commission proportion, but also related with the value of the item auctioned. So, in the practical auctions, the seller and the auction house negotiated with each other to decide the commission rules for their own advantage.

Study on the Winning Probability for a Bid in Procurement Combinational Auction with Tree Structure

In this paper, the processes to determine winning probability for the corresponding bidder's deterministic bid are presented. The analysis of the winning probability is crucial for studying the bidding equilibria and designing the mechanism of procurement combinational auctions (CAs), and it also provides the decision making support for bidders who are in commercial synergies surrounding. Finally, an example is used to illustrate the feasibility and detailed processes of calculating winning probability.

Bargaining strategy of oil companies in international oil and gas development Projects-Based on a bilateral bargaining model

Bargaining between the host country and oil companies is very common to international oil and gas development projects.The existence of information asymmetry gives the host country an endogenous bargaining advantage.Foreign oil companies might change their unfavorable negotiating position by changing the order of bidding and adjusting bidding strategies.This paper introduces both factors into a bilateral bargaining model to study the impact of information asymmetry and bidding order on the strategy and equilibrium returns of oil companies.According to the ownership of the right to bid first,two scenarios are designed for the model to compare the equilibrium returns of the host country and oil companies.The results show that:1)There is a first-mover advantage in the process of bilateral bidding,so oil companies better bid first;2)The information asymmetry will lead to a higher nominal income ratio of oil companies and a lower nominal income ratio of the host country,but it doesn’t affect the total income ratio at all.

Analysis on Pricing Strategy of LNG Truck Trade in China

In 2020,China’s LNG truck consumption reached 36.5 million tons,up 37.5%year-on-year.After a lapse of two years,China’s LNG truck consumption returned to rapid growth;meanwhile,LNG truck trade is also developing rapidly.In this process,the pricing of LNG truck trade also follows different pricing strategies,but there are few public analyses and summaries on which pricing strategy is more suitable for the development of LNG truck trade at this stage.Therefore,this article attempts to summarize the existing domestic LNG truck trade pricing strategy,with a view to offering guidance and insights for the future LNG truck trade participants to better apply the corresponding pricing strategy.

考虑换电需求响应的换电站参与电能量-调频市场竞价策略

电动汽车(EV)大规模发展会加大电网的调峰调频压力。通过换电站市场化运营,可有效激励换电站利用EV换电需求和换电站电池灵活性资源,缓解电网调峰调频压力。为此,提出了换电站参与电能量市场和调频辅助服务市场的协同竞价策略及其两阶段优化模型。第1阶段依据调频市场需求制定换电价格,引导EV调整换电需求,优化冗余电池数量及时序,以便与调频需求趋势一致,进而提升换电站调频服务收益;第2阶段考虑换电站需求响应和市场调峰调频需求,制定换电站竞价策略。所提出竞价策略在优先满足EV换电需求的同时,可降低换电站充电成本、提升调频服务收益,并助力电网调峰调频。算例仿真结果验证了所提策略的有效性。

风氢耦合系统参与现货市场的竞标策略及优化调度方法

为解决高比例风电市场竞标能力弱、市场消纳困难的问题,提出高比例风电耦合氢储能系统联合参与现货市场竞价的双层Stackelberg博弈模型。首先,研究风氢耦合系统内部能量转换关系,揭示风氢耦合系统的能量“时空平移”机理,在综合考虑风氢耦合系统收益和成本的基础上,建立风电投标和氢储能充放电决策模型;其次,兼顾风氢耦合系统和独立系统运营商的利益诉求,基于Stackelberg博弈建立以火电机组、风氢耦合系统为主体的市场出清模型;最后,针对双层模型求解困难的问题,运用KKT条件和强对偶理论将双层模型转换为易于求解的单层混合整数规划模型。仿真结果表明,所提策略对高比例风电的市场消纳起到了积极作用。

新形势下串通投标犯罪数据化侦查实践路径

招投标全流程电子化的日益普及给串通投标犯罪侦查工作带来了一定困难。如何不断提升串通投标犯罪的数据化侦查能力和水平,已经成为全国经济犯罪侦查理论与实务部门广泛关注并亟待解决的问题。本文通过文献研究、案例分析等方法探讨了新形势下我国串通投标犯罪产生的原因、手段及危害,较为系统地阐释了如何综合利用各种数据导侦工具、采用企业基因测序、社会网络分析等方法和技术研判风险企业、分析违法资金流向等问题,深入挖掘显性和隐性犯罪网络,锁定重点人物,构建完整的证据体系,实现对串通投标犯罪的全链条式打击。

考虑冬季供暖的风电—CSP电站联合参与现货市场运行策略

随着“双碳”目标的提出,以风电为代表的可再生能源参与电力现货市场已是大势所趋。但由于具有不确定性和波动性,风电在市场中常处于不利地位。风电与具有灵活调节能力的光热电站(Concentrated Solar Power,CSP)联合能够减少实时出力偏差,进而降低不平衡成本。基于此,本文针对风电—CSP电站联合参与现货市场的运行策略开展研究。首先,对风电—CSP电站联合参与现货市场的机理进行分析,在此基础上,以经济性最优为目标,综合考虑供电收益、冬季供暖收益和不平衡惩罚等因素,提出了考虑冬季供暖的风电—CSP电站联合参与电力现货市场运行策略,并基于Shapley值法对联盟收益进行分配,最后分析了储热容量对联盟收益的影响。算例表明所提联合运行策略能够充分利用CSP电站灵活性,显著提高双方收益,减少弃风损失。

多品种电力市场交易下负荷聚合商投标策略及市场均衡分析

中国新型电力系统建设正面临灵活资源短缺等问题,亟须发挥负荷侧调节能力。随着电网与负荷双向交互越发频繁,更多负荷聚合商通过整合辖区灵活资源参与市场交易,导致以往由发电侧占主导的市场均衡被打破。为研究多主体负荷聚合商博弈策略,构建了能量-备用电力市场下负荷聚合商双层投标模型,并引入多场景描述非决策者投标不确定性对市场均衡的影响。通过强平稳算法(SSM)将多个双层模型转化为带均衡约束的均衡问题(EPEC)模型,并采用大M法、二进制展开法等线性化方法进行线性化。算例结果验证了模型及求解算法的合理性和有效性,表明基于EPEC模型的负荷聚合商最优投标策略更加契合实际市场规则,有助于系统优化负荷曲线,缓解灵活资源供需紧张。

考虑响应违约风险的聚合商双重市场投标策略

负荷聚合商(load aggregator,LA)集成需求侧资源参与双重市场(能量市场和备用市场)投标,需要根据市场运营规则对具有不同行为特性的需求侧资源优化组合。文章考虑双重市场对响应资源的技术要求,从负荷削减量和响应时间2个方面体现需求侧资源的不确定性,引入条件风险价值(conditional value at risk,CVaR)理论,考虑备用市场响应超时的违约风险,制定其在双重市场的资源集成优化投标策略,以响应电量不足期望评估聚合商参与双重市场的响应可靠性。算例分析不同风险偏好系数下聚合商利润与风险的动态关系,优化需求侧资源的容量配置,为聚合商双重市场的投标和风险度量提供参考。

基于多智能体深度确定策略梯度算法的火力发电商竞价策略

火电是新型电力系统的重要支撑,研究火力发电商的竞价策略以及不同出清机制的影响,对保障其低碳高效运营具有重要意义。构建基于多智能体深度确定策略梯度算法的竞价策略模型,分析不同火力发电商组合的竞价差异化策略,优化多主体报价报量策略,探究边际统一出清、按报价支付出清和随机匹配出清3种典型出清机制的市场影响。结果表明,该策略模型可引导火力发电商采取合理的竞价方式以提高市场效率;在新能源渗透率较低时,不同出清机制对各类型机组的影响有所不同;随着新能源渗透率的提高,采用按报价支付出清机制可以兼顾经济和环境效益;当新能源渗透率达到较高水平时,采用随机匹配出清机制可有效应对市场波动风险。

计及需求响应潜力的电动汽车聚合商多主体日前投标策略

针对电动汽车这一新型储能资源大规模接入后电力市场多主体竞争格局,提出一种计及需求响应潜力的电动汽车聚合商多主体日前投标策略。以实现削峰填谷、保障电网安全稳定运行等为目标,促使电网与聚合商等多方市场主体可持续发展,探索其未来商业模式。首先,根据电动汽车用户出行特性进行电动汽车集群分类,考虑出行时间、到达时间及荷电状态等因素,模拟集群内用户随机充放电选择。结合聚合商整体充电功率上限值与电能波动区间值等主要因素,基于历史数据评估聚合商需求响应潜力。然后,考虑多个聚合商投标问题,建立日前市场投标模型,以测算电动汽车聚合商日前市场效益。为探寻多元竞争格局下最优日前市场投标策略,以日前投标净利润最大为目标函数,考虑电量、功率上限及电池容量等约束条件,利用Gurobi对优化问题进行求解。最后,通过湖北省武汉市某区实际负荷算例验证了所提策略的先进性及可行性。

基于主从博弈的虚拟电厂参与多元竞争市场投标策略研究

虚拟电厂(virtual power plant,VPP)可以聚合多元异构分布式能源(distributed energy resource,DER)灵活参与电力市场,但受市场多元主体投标行为不确定性的影响,VPP在日前电力市场面临着潜在的投标需求流标风险。为解决多元竞争电力市场中电价电量不确定性影响下VPP的优化申报问题,提出一种VPP灵活分段投标策略。首先,基于分布式能源运行特性构建了虚拟电厂聚合可调节能力评估方法,在考虑电力平衡需求的基础上,提出按可调节能力划分区间的VPP灵活分段投标策略。然后,构建了虚拟电厂参与日前电力现货市场投标的主从博弈模型,以实现VPP收益及社会效益的最大化。最后,采用强对偶理论和大M法将该均衡约束规划问题(equilibrium problems with equilibrium constraints,EPEC)转化为混合整数线性规划问题(mixed integer linear program,MILP)求解。算例结果表明,VPP采用灵活分段投标策略参与日前电力市场,可以充分利用其可调节能力,保障其投标需求有效中标,有效提升了VPP收益及社会效益。

考虑风电出力和电价不确定性的水风联合现货市场竞价策略

以风电为代表的新能源参与现货市场已是大势所趋,但出力不确定性导致其市场竞争力较弱,而电价的不确定性又进一步增大了其承担的收益风险。联合具有灵活调节能力的水电是有效的解决途径,而制定符合决策者风险意愿的竞价策略则是其中亟须解决的关键问题。为此,从价格接受者角度提出了水电联合风电参与现货市场的竞价模型。模型中采用笛卡尔积组合后的典型场景集描述风电出力、市场电价等多种不确定性,采用条件风险价值衡量不确定性导致的市场风险,并以风险因子作为决策者风险偏好纳入决策过程。通过引入0/1整数变量,将模型中的非线性函数转换为混合整数线性规划模型予以求解。以中国西南某省梯级水电站和风电场为背景,验证了模型的有效性,并进一步分析了风险偏好对竞价决策的影响。