A Large-Scale Spatial Optimization Model of the European Electricity Market

In this paper, we present a large-scale spatial model of the European electricity market including both generation and the physical transmission network (DC Load Flow approach). The model was developed to analyze various questions on market design, congestion management, and investment decisions, with a focus on Germany and Continental Europe. It is a bottom-up model combining electrical engineering and economics: its objective function is welfare maximization, subject to line flow, energy balance, and generation constraints. The model provides simulations on an hourly basis, taking into account variable demand, wind input, unit commitment, start-up costs, pump storage, and other details. Various forms of spatial price discrimination can be implemented, such as locational marginal pricing (“nodal pricing”), or zonal pricing. With over 2,000 nodes and over 3,000 lines, this is one of the largest models developed to date, and allows a highly differentiated spatial analysis. We report modeling results regarding efficient congestion management for Germany and Europe, optimal network expansion under the aspect of increased wind energy production, and the impact of network constraints on location decisions of generation investments.     

Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.     

Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

基于能耗量化传导的WSN路由探测算法

为了降低无线传感器网络(WSN)路由节点的能量损耗,提高网络的寿命周期,需要进行路由节点的优化分布设计。传统方法采用CSMA/CA有限竞争的信道分配模型进行WSN的路由探测算法设计,实现能量均衡,在节点规模较大和干扰较强时,节能的能耗开销较大。提出一种基于能耗量化传导的WSN路由探测算法,首先建立WSN的分簇能耗调度模型,以能量控制开销、丢包率、传输时延等为约束参量指标进行路由探测的控制目标函数的构建,然后采用路由冲突协调机制进行能耗量化分配,结合WSN传输信道的能量传导均衡模型实现WSN路由的优化探测和WSN节点的优化部署。仿真结果表明,采用该方法进行WSN路由探测设计时网络的能效较高,传输时延和误码率等参量指标的表现优于传统方法。     

Energy trading scheme based on consortium blockchain and game theory

With the gradual opening of the electricity sales market, distributed energy trading is becoming an important research topic. However, it is not easy to design practical energy trading schemes in distributed scenario. In particular, known distributed energy trading schemes do not address the security of transaction data and the maximization of benefits among all the participants. In this paper, we propose a distributed energy trading scheme based on consortium blockchain and game theory. In our scheme, a peer-to-peer trading platform is constructed to realize direct transactions among all the participants by the property of decentralization in consortium blockchain. The direct transactions greatly reduce operating costs of energy trading, and at the same time, the security of transaction data can be obtained by the cryptographic techniques such as digital signatures and hash functions associated with the underlying blockchain. Moreover, we design an energy transaction matching mechanism by game theory in our scheme. In the matching mechanism, we construct a game model among all the participants and design an equilibrium solving algorithm, which are the key techniques to realize the maximization of benefits among all the participants in energy trading. The security analysis and experimental results show that our scheme can realize the best transaction price and quantity in the transaction matching and has high security in distributed energy transaction scenarios.     

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.     

Electric Power Network Oligopoly as a Dynamic Stackelberg Game

Over the last two decades, the electricity industry has shifted from regulation of monopolistic and centralized utilities towards deregulation and promoted competition. With increased competition in electric power markets, system operators are recognizing their pivotal role in ensuring the efficient operation of the electric grid and the maximization of social welfare. In this article, we propose a hypothetical new market of dynamic spatial network equilibrium among consumers, system operators and electricity generators as solution of a dynamic Stackelberg game. In that game, generators form an oligopoly and act as Cournot-Nash competitors who non-cooperatively maximize their own profits. The market monitor attempts to increase social welfare by intelligently employing equilibrium congestion pricing anticipating the actions of generators. The market monitor influences the generators by charging network access fees that influence power flows towards a perfectly competitive scenario. Our approach anticipates uncompetitive behavior and minimizes the impacts upon society. The resulting game is modeled as a Mathematical Program with Equilibrium Constraints (MPEC). We present an illustrative example as well as a stylized 15-node network of the Western European electric grid.     

Dynamic Spatial Auction Market Models with General Cost Mappings

The deregulation of electricity markets in Europe has deeply changed the organization of this sector. Vertically integrated generating companies have been unbundled to create competition and to increase the competitiveness of electricity markets. Directive 96/92/EC was issued by the European Commission to liberalize electricity markets and to pave the way for the creation of the Internal Electricity Market. In particular, this Directive aimed at promoting the competition in the activities of electricity generation and wholesale through the creation of a “marketplace” and the maximization of transparency and efficiency. Competition in European day-ahead electricity markets has been established through auction markets where electricity producers and consumers offer/bid prices and volumes. This paper suggests a dynamic equilibrium model for a system of auction markets linked by transmission lines and subject to energy balance and transmission constraints, such as those characterizing restructured electricity markets. This model is treated as a system of variational inequalities with arbitrary monotone mappings. An inexact splitting type method is proposed to find its solution. Numerical experiments are conducted on the Italian day-ahead electricity market.     

OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

Generation strategies for gaming transmission constraints: will the deregulated electric power market be an oligopoly?

Constrained transmission lines are known to be able to economically isolate submarkets from the competition of players located elsewhere on the network. This paper examines the type of oligopolistic competition that is likely to take place in these submarkets. It shows, using simple models, how static or intertemporal Nash equilibria can rise in a framework of price or supply function competitions, found to be more realistic than Cournot models in the particular case of short-term competition in the electric power market. This paper shows also how transmission constraints can play a direct role in the outcome of the oligopolistic competition and encourage strategic behavior by the generators. Transmission lines that would not be constrained if the players did not know of their thermal limits may be strategically driven to operate at these limits in order to maximize the profits of the players who have market power, leaving the others to cope with the consequences of such behavior.     

Information markets over trust networks

Information markets are inefficient. Information products have large upfront development costs, yet their duplication costs are negligibly small; and they are experience goods with high costs of marketing and promotion. As a result, either winner-take-all markets are created through large and expensive promotional campaigns, or artificial monopoly power is conferred by the government through copyright protection, or both, to prevent the collapse of these markets from intense price competition and piracy. Such inefficiency creates opportunities to design more efficient markets by utilizing new technologies. Trust networks provide such an opportunity where the network infrastructure acts not only as a distribution system for information products, but also as an advertising and promotion medium, a payment and pricing mechanism, a guarantee and insurance service, and a copyright enforcement and dispute resolution tool. Such a network-centric market place is proposed to remedy many of the shortcomings of mass markets by relying on peer-to-peer distribution, peer-to-peer payments, and peer-to-peer enforcement of trust and integrity. Analytical models are presented to show that such a market place for information goods can scale up to satisfy large markets without expensive promotions and advertising campaigns, create customized products with dynamic pricing, reduce entry costs by eliminating the distinction between buyers and sellers, and eliminate the need for copyright protection.     

Network Expansion to Mitigate Market Power

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.     

Modeling and analysis of the effects of QoS and reliability on pricing, profitability, and risk management in multiperiod grid-computing networks

In this paper we develop a network equilibrium model for optimal pricing and resource allocation in Computational Grid Network. We consider a general network economy model with Grid Resource Providers, Grid Resource Brokers and Grid Users. The proposed framework allows for the modeling and theoretical analysis of Computational Grid Markets that considers a non-cooperative behavior of decision-makers in the same tier of the grid computing network (such as, for example, Grid Resource Providers) as well as cooperative behavior between tiers (between Resource Providers and Grid Brokers). We introduce risk management into the decision making process by analyzing the decision-marker's reliability and quality of service (QoS) requirement. We analyze resource allocation patterns as well as equilibrium price based on demand, supply, and cost structure of the grid computing market network. We specifically answer the following questions with several numerical examples: How do system reliability levels affect the QoS levels of the service providers and brokers under competition? How do system reliability levels affect the profits of resource providers and brokers in a competitive market? How do system reliability levels influence the pricing of the services in a competitive environment? How do users' service request types, QoS requirements, and timing concerns affect users' behaviors, costs and risks in equilibrium? How does the market mechanism allocate resources to satisfy the demands of users? We find that for users who request same services certain timing flexibility can not only reduce the costs but also lower the risks. The results indicated that the value of QoS can be efficiently priced based on the heterogeneous service demands.     

基于模糊层次分析法的铁路运价自动定价方法研究

通过分析影响铁路运价定价的各种因素,选取了其中的运营成本、国家价格策略、货主市场承受能力、平行运行径路的竞争、附加成本、盈利水平、供求关系等7个影响因素,运用模糊层次分析方法,确定了各运价定价影响因素的指标权重,从而为铁路企业在新建铁路运价定价过程中提供决策依据和参考标准.     

Price strategies in dynamic duopolistic markets with deregulated electricity supplies using mixed strategies

While effective competition can force service providers to seek economically efficient methods to reduce costs, the deregulated electricity supply industry still allows some generators to exercise market power at particular locations, thereby preventing the deregulated power market to be perfectly competitive. In this paper, we investigate the interdependence of pricing mechanisms and strategy behaviors of the suppliers. A multiperiod dynamic profit-maximizing problem is converted to a bimatrix game that is solved in the framework of mixed strategies. By this procedure, we have at least one Nash solution. Instead of considering only perfectly competitive price and monopoly price, we introduce other prices between these two to simulate the real market better. Numerical examples show that the new entrant that maximizes its profit will not choose the perfectly competitive price even as an entry price.     

Using Mathematical Programming for Electricity Spot Pricing

Recent moves around the world to introduce competition into electricity markets have created a need for mechanisms to determine electricity spot prices which provide good incentives for market coordination. Duality theory suggests that such prices can be found by solving a mathematical program. We derive implicit prices corresponding to an actual half-hourly dispatch of a full a.c. power system, and discuss the application of spot pricing in New Zealand and the United States.     

基于Agent的电力市场价格仿真研究

电力市场是规模大、层次复杂的系统,电力系统的改革的重点是建立一个竞争机制的电力市场。传统电力系统仿真工具已经难以分析市场中复杂的关系,采用Agent技术仿真已成为复杂系统建模的有效工具。本文对Agent平台上的电力市场价格模拟系统进行研究,模拟电价产生中发电侧、输电侧和用户的竞争,该方法可用来分析多个参与者对电价形成的影响。     

Market power and welfare effects in DC power flow electricity models with thermal line losses

A nodal electric power network with Cournot–Nash interaction among power generators is formulated as a mixed complementarity problem. The model incorporates a direct current (DC) power flow approximation with thermal line losses to model real-time flows. We include constant wheeling rate and variable congestion charges for transmission of electricity. Market power and welfare effects are measured in an aggregated Indiana electric grid model. We find that imposing DC power flow constraints in a model results in significant changes in social welfare estimates. Line losses are also an important factor affecting market power and welfare of market participants in the case study.     

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response(DR) using shared energy storage systems(ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets...     

可靠通信的多跳水声网络能量最小路径

目前对水声网络能耗的研究并未考虑因数据发送失败而引起的数据重发导致的额外的能耗。研究在给定接收信噪比条件下可靠通信的多跳水声网络能量最小路径问题。首先建立了可靠通信的网络能量模型,通过曲线拟合的方法得到最优频率-距离关系的近似表达,以简化能耗模型;然后分析了可变发送功率和固定发送功率两种模式下的能量最小路径,从理论上证明了直线等距网络的总能耗最小,并给出了直线等距网络最优跳数和最优距离的求解方法。仿真结果验证了该理论的正确性。