Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modeled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets Assisted by CPS

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy assisted by a cyber-physical system for supporting management decisions to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a stochastic linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modelled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

MASGriP – a Multi-Agent Smart Grid Simulation Platform

The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP – A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Energy and ancillary services joint market simulation

In order to develop a flexible simulator, a variety of models for Ancillary Services (AS) negotiation has been implemented in MASCEM – a multi-agent system competitive electricity markets simulator. In some of these models, the energy and the AS are addressed simultaneously while in other models they are addressed separately. This paper presents an energy and ancillary services joint market simulation. This paper proposes a deterministic approach for solving the energy and ancillary services joint market. A case study based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve, and Non-Spinning Reserve services is used to demonstrate that the use of the developed methodology is suitable for solving this kind of optimization problem. The presented case study is based on CAISO real AS market data considers fifteen bids.

Seller, buyer and VPP agents behavior on a simulated electricity market

Competitive 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 an electricity market simulator able to model market players and simulate their operation in the market. As market players are complex entities, having their characteristics and objectives, making their decisions and interacting with other players, a multi-agent architecture is used and proved to be adequate. MASCEM players have learning capabilities and different risk preferences. They are able to refine their strategies according to their past experience (both real and simulated) and considering other agents’ behavior. Agents’ behavior is also subject to its risk preferences.

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.

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.

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Power Producers Trading Electricity in Both Pool and Forward Markets

The electricity industry throughout the world, which has long been dominated by vertically integrated utilities, has experienced major changes. Deregulation, unbundling, wholesale and retail wheeling, and real-time pricing were abstract concepts a few years ago. Today market forces drive the price of electricity and reduce the net cost through increased competition. As power markets continue to evolve, there is a growing need for advanced modeling approaches. This article addresses the challenge of maximizing the profit (or return) of power producers through the optimization of their share of customers. Power producers have fixed production marginal costs and decide the quantity of energy to sell in both day-ahead markets and a set of target clients, by negotiating bilateral contracts involving a three-rate tariff. Producers sell energy by considering the prices of a reference week and five different types of clients with specific load profiles. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

A trader portfolio optimization of bilateral contracts in electricity retail markets

Electricity markets are systems for effecting the purchase and sale of electricity using supply and demand to set energy prices. Two major market models are often distinguished: pools and bilateral contracts. Pool prices tend to change quickly and variations are usually highly unpredictable. In this way, market participants often enter into bilateral contracts to hedge against pool price volatility. This article addresses the challenge of optimizing the portfolio of clients managed by trader agents. Typically, traders buy energy in day-ahead markets and sell it to a set of target clients, by negotiating bilateral contracts involving three-rate tariffs. Traders sell energy by considering the prices of a reference week and five different types of clients. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

Power producers trading electricity in both pool and forward markets

The electricity industry throughout the world, which has long been dominated by vertically integrated utilities, has experienced major changes. Deregulation, unbundling, wholesale and retail wheeling, and real-time pricing were abstract concepts a few years ago. Today market forces drive the price of electricity and reduce the net cost through increased competition. As power markets continue to evolve, there is a growing need for advanced modeling approaches. This article addresses the challenge of maximizing the profit (or return) of power producers through the optimization of their share of customers. Power producers have fixed production marginal costs and decide the quantity of energy to sell in both day-ahead markets and a set of target clients, by negotiating bilateral contracts involving a three-rate tariff. Producers sell energy by considering the prices of a reference week and five different types of clients with specific load profiles. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.