Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

VPP´s multi-level negotiation in smart grids and competitive electricity markets

The increase of distributed generation (DG) has brought about new challenges in electrical networks electricity markets and in DG units operation and management. Several approaches are being developed to manage the emerging potential of DG, such as Virtual Power Players (VPPs), which aggregate DG plants; and Smart Grids, an approach that views generation and associated loads as a subsystem. This paper presents a multi-level negotiation mechanism for Smart Grids optimal operation and negotiation in the electricity markets, considering the advantages of VPPs’ management. The proposed methodology is implemented and tested in MASCEM – a multiagent electricity market simulator, developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.

Ancillary service market simulation

Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper deals with ancillary services negotiation in electricity markets. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of ancillary services using two different methods (Linear Programming and Genetic Algorithm approaches) is included in the paper.

Dispachability improvement of wind generation by the virtual power producers

Nowadays, there is a growing environmental concern about were the energy that we use comes from, bringing the att ention on renewable energies. However, the use and trade of renewable e nergies in the market seem to be complicated because of the lack of guara ntees of generation, mainly in the wind farms. The lack of guarantees is usually addressed by using a reserve generation. The aggregation of DG p lants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of wind generation technologies, making them valuable in electricity markets. This paper presents some resul ts obtained with a simulation tool (ViProd) developed to support VPPs in the analysis of their operation and management methods and of their strat egies effects.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Abordagem à previsão do preço de energia elétrica via métodos de suavização exponencial

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Estratégias de comercialização de energia elétrica em mercados liberalizados

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Impact of a price-maker pumped storage hydro unit on the integration of wind energy in power systems

The increasing integration of larger amounts of wind energy into power systems raises important operational issues, such as the balance between power generation and demand. The pumped storage hydro (PSH) units are one possible solution to mitigate this problem, once they can store the excess of energy in the periods of higher generation and lower demand. However, the behaviour of a PSH unit may differ considerably from the expected in terms of wind power integration when it operates in a liberalized electricity market under a price-maker context. In this regard, this paper models and computes the optimal PSH weekly scheduling in a price-taker and price-maker scenarios, either when the PSH unit operates in standalone and integrated in a portfolio of other generation assets. Results show that the price-maker standalone PSH will integrate less wind power in comparison with the price-taker situation. Moreover, when the PSH unit is integrated in a portfolio with a base load power plant, the role of the price elasticity of demand may completely change the operational profile of the PSH unit. (C) 2014 Elsevier Ltd. All rights reserved.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.