Electricity Market Simulation Research Articles

A Trading Simulator Model for the Wholesale Electricity Market

Currently, to meet the requirements of modern power systems as fully and efficiently as possible, the electricity markets have diversified greatly. Under these conditions, it becomes difficult for a producer to determine the structure of transactions that is financially optimal. Starting from the operational rules of the power systems that have shaped the electricity markets structure, the objective of this paper is to develop an electricity market simulator model that includes the basics of a best practice guide for producers that compete on various electricity markets to carry out the trading activities and enhance their financial results. The market simulator model considers both the bilateral long- or mid-term agreements and short-term offers on day-ahead, ancillary services and balancing markets providing the entire trading scenario and associated cash-flow and risks. Its significance consists in assisting the producer to plan its resources and create projections by performing multiple trading scenarios and selecting the best one. Thus, this paper proposes to uncover constraints and business rules for a simulator model assisting the market players to access the electricity markets and select the best option using Multiple-Criterial Decision-Making (MCDM) methods (Electre, Topsis, Analytical Hierarchy Process) or the weighted Euclidean distance. The simulations comprise four trading scenarios for different types of producers (gas or fossil-powered generators) generating 100 MW, that are ordered by independent criteria. The results obtained with MCDM and the proposed method showed that they indicated the same scenario as the best trading option based on the type of the producer.

A Counterfactual Analysis of Regional Renewable Energy Auctions Taking the Spatial Dimension into Account

Auctions have recently been introduced in many countries as a useful alternative to accomplish renewable energy supports. However, they are often accompanied by a high concentration of renewable energy power plants at productive sites, at the expense of other, less favorable sites. This paper studies the impact of alternative renewable energy auction designs on the promotion of renewable energy in Germany using a novel multi-level approach. To do so, we analyze auctions on three different spatial levels: the national auction, the regional auction-type I (federal states level) and the regional auction-type II (north/south auction). The initial step for modeling renewable energy auction schemes in Germany is to investigate the onshore wind potential with a high regional and temporal resolution, using the GIS-data-based tool REMix-ENDAT. The results of this analysis show a considerable as yet untapped onshore wind potential in the southern federal states of Germany. Second, an onshore wind auction model is developed using a system dynamics approach. Finally, the comparison of the support payment for different auction designs is evaluated using the electricity market simulation model (HECTOR). Findings indicate that more bidders from the southern federal states can win in the regional auctions. Detailed spatial analysis reveals a trade-off between balanced diversity of bidders and average auction price (the price difference varies from 0.5 €-ct/kWh to 1.5 €-ct/kWh in different scenarios). We conduct that, a noticeable potential support payment saving from regional auctions should be exploited in long-term renewable energy policy designs.

Computationally Efficient Market Simulation Tool for Future Grid Scenario Analysis

This paper proposes a computationally efficient electricity market simulation tool (MST) suitable for future grid scenario analysis. The market model is based on a unit commitment (UC) problem and takes into account the uptake of emerging technologies, like demand response, battery storage, concentrated solar thermal generation, and HVDC transmission. To allow for a subsequent stability assessment, the MST requires an explicit representation of the number of online generation units, which affects power system inertia and reactive power support capability. These requirements render a full-fledged UC model computationally intractable, so we propose unit clustering, a rolling horizon approach, and constraint clipping to increase the computational efficiency. To showcase the capability of the proposed tool, we use a simplified model of the Australian National Electricity Market with different penetrations of renewable generation. The results are verified by a comparison to a more expressive and computationally intensive binary UC, which confirm the validity of the approach for long term future grid studies.

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy generation and market prices variation. Risk assessment and management as integrated part of actual market negotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players’ strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players’ behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company’s scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company’s evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from competitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players’ actions according to the participation risk.

Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems

Day-to-day operation of the electricity grid generation, transmission, and distribution is environmentally driven and closely dependent on evolving weather patterns. This paper introduces several new weather-driven analytics for accurate spatial–temporal electricity generation forecasts, asset health and reliability assessment, probabilistic load forecasts, and electricity market simulations. A new risk metric is suggested, which accounts for the weather hazards, grid vulnerability, and financial consequences in the face of changing weather patterns and associated meteorological predictions over time. New mitigation formulations for power system topology control through transmission line switching for fast and timely recovery of the weather-caused electricity outages are suggested. The proposed decision support tool enables the operators to predictively evaluate the high-risk weather threats and consequently plan on how to safeguard the grid when exposed to forecasted weather-driven incidents. The efficiency of the proposed toolset is illustrated by application to a part of the IEEE 73-Bus test system.

Iberian electricity market ontology to enable smart grid market simulation

Several approaches have been proposed to enhance the potential of distributed generation (DG). Some of the most prominent solutions include the aggregation of DG units and other players, culminating in the concept of and Smart Grid (SG). In this context, several simulation tools arose to study and test the new market mechanisms. However, all of these simulators are closed and centred in their object of study, neglecting the potential advantages of interoperating with other systems from the same domain. This work proposes the use of ontologies for systems interoperability in the power and energy systems domain. The ontologies have been developed and implemented in MASCEM and MASGriP - multi-agent simulators of electricity markets, and SG operation and management,respectively; thus enabling joint electricity market and SG simulations.

Is District Heating Combined Heat and Power at Risk in the Nordic Area?—An Electricity Market Perspective

The Nordic power market has exceptionally low carbon emissions. Energy efficient combined heat and power (CHP) plays an important role in the market, and also produces a large share of Nordic district heating (DH) energy. In future Nordic energy systems, DH CHP is often seen as vital for flexibility in electricity production. However, CHP electricity production may not be profitable in the future Nordic market. Even currently, the prevailing trend is for CHP plants to be replaced with heat-only boilers in DH production. In this work, we aim to describe the future utilisation of CHP in the Nordic area. We use an electricity market simulation model to examine the development of the Nordic electricity market until 2030. We examine one main projection of electricity production capacity changes, and based on it we assess scenarios with different electricity demands and CO2 emission prices. Differences between scenarios are notable: For example, the stalling or increasing of electricity demand from the 2014 level can mean a difference of 15 €/MWh in the average market price of electricity in 2020. The results of this paper underline the importance of considering several alternative future paths of electricity production and consumption when designing new energy policies.

Incentivizing smart charging: Modeling charging tariffs for electric vehicles in German and French electricity markets

Over the past few years, registration figures of plug-in electric vehicles have increased rapidly in industrialized countries. This could cause considerable mid- to long-term effects on electricity markets. To tackle potential challenges specific to electric power systems, we develop a load-shift-incentivizing electricity tariff that is suitable for electric vehicle users and analyze the tariff scheme in three parts. First, acceptance is analyzed based on surveys conducted among fleet managers and electric vehicle users. Corresponding results are used to calibrate the tariff. Secondly, load flexibilities of electric vehicle charging are used in an agent-based electricity market simulation model of the French and German wholesale electricity markets to simulate corresponding market impacts. Thirdly, the charging manager’s (‘aggregator’) business model is analyzed. Our results reveal that the tariff is highly suitable for incentivizing vehicle users to provide load flexibilities, which consequently increase the contribution margins of the charging managers. The main drawback is the potential for ‘avalanche effects’ on wholesale electricity markets increasing charging mangers’ expenditures, especially in France.

Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP) and each rival’s bidding strategy as common knowledge (in dynamic game market models), and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models), which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC) algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n). The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare compared with traditional reinforcement learning methods.

MASCEM: Optimizing the performance of a multi-agent system

The electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of supporting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

The market (in-)stability reserve for EU carbon emission trading: Why it might fail and how to improve it

The EU parliament has accepted a proposal of the EU commission on the backloading of EU emission allowances (EUA), where the auctioning of EUAs is postponed to future time periods. The EU commission has also proposed a market stability reserve (MSR), which is a quantity-based stabilisation policy that is aimed at controlling the volume of EUAs in circulation.Using an agent-based electricity market simulation with endogenous investment and a CO2 market (including banking), we analyse the backloading reform and the proposed MSR. We find backloading to only have a short-term impact of CO2 prices; regardless, there is a significant risk of high CO2 prices and volatility in the EU ETS.Our simulations indicate that the triggers of the proposed MSR appear to be set too low for the hedging need of power producers, effectively leading to a stricter cap in its initial 10–15 years of operation. While the current proposal may be improved by choosing different triggers, a reserve that is based on volume triggers is likely to increase price volatility, contrary to its purpose. Additional problems are the two-year delay in the response time and the abruptness of the response function, combined with the difficulty of estimating future hedging behaviour.

Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach

The recent low CO2 prices in the European Union Emission Trading Scheme (EU ETS) have triggered a discussion whether the EU ETS needs to be adjusted. We study the effects of CO2 price floors and a price ceiling on the dynamic investment pathway of two interlinked electricity markets (loosely based on Great Britain, which already has introduced a price floor, and on Central Western Europe). Using an agent-based electricity market simulation with endogenous investment and a CO2 market (including banking), we analyse the cross-border effects of national policies as well as system-wide policy options.A common, moderate CO2 auction reserve price results in a more continuous decarbonisation pathway. This reduces CO2 price volatility and the occurrence of carbon shortage price periods, as well as the average cost to consumers. A price ceiling can shield consumers from extreme price shocks. These price restrictions do not cause a large risk of an overall emissions overshoot in the long run. A national price floor lowers the cost to consumers in the other zone; the larger the zone with the price floor, the stronger the effect. Price floors that are too high lead to inefficiencies in investment choices and to higher consumer costs.

Modelling renewable energy impact on the electricity market in India

Renewable power generation development, most notably for wind and solar, has taken off at a rapid pace in India especially in the last 4 years. While these developments have many positive aspects, a rapid shift in balance of baseload and intermittent generation must be assessed carefully to ensure the share of renewable power generation increases without compromising system security and economics. Seasonal and spatial variability of wind, and to a lesser extent that of solar, can render these resources to have low availability for a significant part of the year leading to an increase in unserved energy, i.e., deteriorate system reliability. The intermittency of generation also impacts on inter-state power flows and lead to higher congestion in the grid. Climate model results provide a rich set of information on the nature of solar/wind variability that can be embedded in an electricity market simulation tool to assess these impacts on prices, generation dispatch and power flows. We have developed a modelling analysis for the Indian national electricity market informed by CSIRO climate model results. We have assessed the added costs arising from intermittency to put in perspective the true costs and benefits of renewable power. We have focused on the near-term developments in 2017 to show how some of the high renewable growth scenarios included in the Indian National Electricity Plan may imply significant pressure on inter-state/region transfer capability, and lead to a significant worsening of system reliability. The outcome of our modelling analysis suggests that a more orderly and balanced development of renewable and conventional power generation capacity is needed with a stronger focus on system economics and security.

The magnitude of the impact of a shift from coal to gas under a Carbon Price

We seek to evaluate the extent of the pass through of increased fuel and carbon costs to wholesale prices with a shift of generation from coal-fired to gas-fired plants. Modelling of Australia's National Electricity Market in 2035 is undertaken using Australian Energy Market Operator assumptions for fuel costs, capital costs and demand forecasts. An electricity market simulation package (PLEXOS), which uses deterministic linear programming techniques and transmission and generating plant data, is used to optimize the power system and determine the least cost dispatch of generating resources to meet a given demand. We find that wholesale market prices increase due to the full pass through of the increased costs of gas over coal as an input fuel and the Carbon Price. In addition, we find that wholesale prices increase by more than the pass through of fuel and carbon costs because of the fact that generators can charge infra-marginal rents and engage in strategic behaviour to maximize their profits.

Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its app lication in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players' actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. 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. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Reliability in the U.S. electricity industry under new environmental regulations

Implementation of new environmental regulations of sulfur dioxide, nitrogen oxides and mercury in the U.S. electricity industry has triggered concerns about system reliability. Results from a national electricity market simulation model suggest that these regulations lead to little change in generation capacity and are unlikely to create the shock to the system that some anticipate. Large costs of investments in pollution controls are partially offset by a lower cost burden for tradable emissions allowances. The combined effects result in a 1 percent increase in national average retail electricity prices. In 2020 producers pay approximately 30 percent and consumers pay approximately 70 percent of the total costs of the regulations, which equal between $6.6 and $7.1 billion in 2020 (real 2009$). The regulation leads to substantial reductions in emissions of mercury and sulfur dioxide from the electricity sector.

Short-Term Electricity Market Simulation for Pool-Based Multi-Period Auctions

This paper presents a simulation model based on the diagonalization algorithm for the study of the short-term operation of a pool-based oligopolistic electricity market with multi-period auctions. A detailed producer's profit maximization model as well as a detailed non-convex independent system operator (ISO) market clearing algorithm are used. A multi-period auction is assumed, where generators, under conditions of perfect information (regarding system load and competitor offers), submit stepwise energy and reserves offer curves and, subsequently, the ISO clears the market. An innovative correction of the residual demand curve (RDC) is introduced to account for the effect of some of the non-convexities of the ISO market clearing. Convergence of the diagonalization algorithm is enhanced using reinforcement learning (RL), through which the knowledge on the success of the producers' past behavior is used to adjust the supply offers.

The impact of electricity demand reduction policies on the EU-ETS: Modelling electricity and carbon prices and the effect on industrial competitiveness

The European electricity market is linked to a carbon market with a fixed cap that limits greenhouse gas emissions. At the same time, a number of energy efficiency policy instruments in the EU aim at reducing the electricity consumption. This article explores the interactions between the EU's carbon market on the one hand and instruments specifically targeted towards energy end-use efficiency on the other hand. Our theoretical analysis shows how electricity demand reduction triggered by energy efficiency policy instruments affects the emission trading scheme. Without adjustments of the fixed cap, decreasing electricity demand (relative to business-as-usual) reduces the carbon price without reducing total emissions. With lower carbon prices, costly low emission processes will be substituted by cheaper high emitting processes. Possible electricity and carbon price effects of electricity demand reduction scenarios under various carbon caps are quantified with a long-term electricity market simulation model. The results show that electricity efficiency policies allow for a significant reduction of the carbon cap. Compared to the 2005 emission level, 30% emission reductions can be achieved by 2020 within the emission trading scheme with similar or even lower costs for the industrial sector than were expected when the cap was initially set for a 21% emission reduction.

Suitability assessment framework of agent-based software architectures

ContextA common distributed intelligent system architecture is Multi Agent Systems (MASs). Creating systems with this architecture has been recently supported by Agent Oriented Software Engineering (AOSE) methodologies. But two questions remain: how do we determine the suitability of a MAS implementation for a particular problem? And can this be determined without AOSE expertise? ObjectiveGiven the relatively small number of software engineers that are AOSE experts, many problems that could be better solved with a MAS system are solved using more commonly known but not necessarily as suitable development approaches (e.g. object-oriented). The paper aims to empower software engineers, who are not necessarily AOSE experts, in deciding whether or not they should advocate the use of an MAS technology for a given project. MethodThe paper will construct a systematic framework to identify key criteria in a problem requirement definition to assess the suitability of a MAS solution. The criteria are first identified using an iterative process. The features are initially identified from MAS implementations, and then validated against related work. This is followed by a statistical analysis of 25 problems that characterise agent-oriented solutions previously developed to group features into key criteria. ResultsKey criteria were sufficiently prominent using factor analysis to construct a framework which provides a process that identifies within the requirements the criteria discovered. This framework is then evaluated for assessing suitability of a MAS architecture, by non-AOSE experts, on two real world problems: an electricity market simulation and a financial accounting system. ConclusionSubstituting a software engineer’s personal inclination to (or not to) use a MAS, our framework provides an objective mechanism. It can supplant current practices where the decision to use a MAS architecture for a given problem remains an informal process. It was successfully illustrated on two real world problems to assess the suitability of a MAS implementation. This paper will potentially facilitate the take up of MAS technology.