Elspot: Nord Pool Spot Integration in MASCEM Electricity Market Simulator

The energy sector in industrialized countries has been restructured in the last years, with the purpose of decreasing electricity prices through the increase in competition, and facilitating the integration of distributed energy resources. However, the restructuring process increased the complexity in market players' interactions and generated emerging problems and new issues to be addressed. In order to provide players with competitive advantage in the market, decision support tools that facilitate the study and understanding of these markets become extremely useful. In this context arises MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), a multi-agent based simulator that models real electricity markets. To reinforce MASCEM with the capability of recreating the electricity markets reality in the fullest possible extent, it is crucial to make it able to simulate as many market models and player types as possible. This paper presents a new negotiation model implemented in MASCEM based on the negotiation model used in day-ahead market (Elspot) of Nord Pool. This is a key module to study competitive electricity markets, as it presents well defined and distinct characteristics from the already implemented markets, and it is a reference electricity market in Europe (the one with the larger amount of traded power).

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Ontologies for the interoperability of multiagent electricity markets simulation platforms

Electricity markets worldwide are complex and dynamic environments with very particular characteristics. These are the result of electricity markets’ restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Market players and regulators are very interested in predicting the market’s behaviour. Market players need to understand the market behaviour and operation in order to maximize their profits, while market regulators need to test new rules and detect market inefficiencies before they are implemented. The growth of usage of simulation tools was driven by the need for understanding those mechanisms and how the involved players' interactions affect the markets' outcomes. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Still, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This dissertation proposes the development and implementation of ontologies for semantic interoperability between multi-agent simulation platforms in the scope of electricity markets. The added value provided to these platforms is given by enabling them sharing their knowledge and market models with other agent societies, which provides the means for an actual improvement in current electricity markets studies and development. The proposed ontologies are implemented in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) and tested through the interaction between MASCEM agents and agents from other multi-agent based simulators. The implementation of the proposed ontologies has also required a complete restructuring of MASCEM’s architecture and multi-agent model, which is also presented in this dissertation. The results achieved in the case studies allow identifying the advantages of the novel architecture of MASCEM, and most importantly, the added value of using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from the various systems.

Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling

The electricity market restructuring, and its worldwide evolution into regional and even continental scales, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in a rising complexity in power systems operation. Several power system simulators have been developed in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex and constantly changing environment. The main contribution of this paper is given by the integration of several electricity market and power system models, respecting to the reality of different countries. This integration is done through the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The continuous development of Multi-Agent System for Competitive Electricity Markets platform provides the means for the exemplification of the usefulness of this ontology. A case study using the proposed multi-agent platform is presented, considering a scenario based on real data that simulates the European Electricity Market environment, and comparing its performance using different market mechanisms. The main goal is to demonstrate the advantages that the integration of various market models and simulation platforms have for the study of the electricity markets’ evolution.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.

Multi-agent system for personalization of location-based services

The current ubiquitous network access and increase in network bandwidth are driving the sales of mobile location-aware user devices and, consequently, the development of context-aware applications, namely location-based services. The goal of this project is to provide consumers of location-based services with a richer end-user experience by means of service composition, personalization, device adaptation and continuity of service. Our approach relies on a multi-agent system composed of proxy agents that act as mediators and providers of personalization meta-services, device adaptation and continuity of service for consumers of pre-existing location-based services. These proxy agents, which have Web services interfaces to ensure a high level of interoperability, perform service composition and take in consideration the preferences of the users, the limitations of the user devices, making the usage of different types of devices seamless for the end-user. To validate and evaluate the performance of this approach, use cases were defined, tests were conducted and results gathered which demonstrated that the initial goals were successfully fulfilled.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Electricity markets modeling considering complex contracts and aggregators

All over the world, the liberalization of electricity markets, which follows different paradigms, has created new challenges for those involved in this sector. In order to respond to these challenges, electric power systems suffered a significant restructuring in its mode of operation and planning. This restructuring resulted in a considerable increase of the electric sector competitiveness. Particularly, the Ancillary Services (AS) market has been target of constant renovations in its operation mode as it is a targeted market for the trading of services, which have as main objective to ensure the operation of electric power systems with appropriate levels of stability, safety, quality, equity and competitiveness. In this way, with the increasing penetration of distributed energy resources including distributed generation, demand response, storage units and electric vehicles, it is essential to develop new smarter and hierarchical methods of operation of electric power systems. As these resources are mostly connected to the distribution network, it is important to consider the introduction of this kind of resources in AS delivery in order to achieve greater reliability and cost efficiency of electrical power systems operation. The main contribution of this work is the design and development of mechanisms and methodologies of AS market and for energy and AS joint market, considering different management entities of transmission and distribution networks. Several models developed in this work consider the most common AS in the liberalized market environment: Regulation Down; Regulation Up; Spinning Reserve and Non-Spinning Reserve. The presented models consider different rules and ways of operation, such as the division of market by network areas, which allows the congestion management of interconnections between areas; or the ancillary service cascading process, which allows the replacement of AS of superior quality by lower quality of AS, ensuring a better economic performance of the market. A major contribution of this work is the development an innovative methodology of market clearing process to be used in the energy and AS joint market, able to ensure viable and feasible solutions in markets, where there are technical constraints in the transmission network involving its division into areas or regions. The proposed method is based on the determination of Bialek topological factors and considers the contribution of the dispatch for all services of increase of generation (energy, Regulation Up, Spinning and Non-Spinning reserves) in network congestion. The use of Bialek factors in each iteration of the proposed methodology allows limiting the bids in the market while ensuring that the solution is feasible in any context of system operation. Another important contribution of this work is the model of the contribution of distributed energy resources in the ancillary services. In this way, a Virtual Power Player (VPP) is considered in order to aggregate, manage and interact with distributed energy resources. The VPP manages all the agents aggregated, being able to supply AS to the system operator, with the main purpose of participation in electricity market. In order to ensure their participation in the AS, the VPP should have a set of contracts with the agents that include a set of diversified and adapted rules to each kind of distributed resource. All methodologies developed and implemented in this work have been integrated into the MASCEM simulator, which is a simulator based on a multi-agent system that allows to study complex operation of electricity markets. In this way, the developed methodologies allow the simulator to cover more operation contexts of the present and future of the electricity market. In this way, this dissertation offers a huge contribution to the AS market simulation, based on models and mechanisms currently used in several real markets, as well as the introduction of innovative methodologies of market clearing process on the energy and AS joint market. This dissertation presents five case studies; each one consists of multiple scenarios. The first case study illustrates the application of AS market simulation considering several bids of market players. The energy and ancillary services joint market simulation is exposed in the second case study. In the third case study it is developed a comparison between the simulation of the joint market methodology, in which the player bids to the ancillary services is considered by network areas and a reference methodology. The fourth case study presents the simulation of joint market methodology based on Bialek topological distribution factors applied to transmission network with 7 buses managed by a TSO. The last case study presents a joint market model simulation which considers the aggregation of small players to a VPP, as well as complex contracts related to these entities. The case study comprises a distribution network with 33 buses managed by VPP, which comprises several kinds of distributed resources, such as photovoltaic, CHP, fuel cells, wind turbines, biomass, small hydro, municipal solid waste, demand response, and storage units.

Multi-agent simulation of continental, regional, and micro electricity markets

Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi- Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Cooperative scheduling system with emergent swarm based behavior

This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

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 simu-lator 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 pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.