Effects of herding behavior of tradable green certificate market players on market efficiency: Insights from heterogeneous agent model

Tradable green certificate (TGC) scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment. TGC market efficiency is reflected in stimulating renewable energy investment, but may be reduced by the herding behavior of market players. This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents, communication structure, and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency. The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency, especially when the borrowing is allowed. In addition, the fundamental strategy is diffused by herding evolution, but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism. Moreover, the herding behavior may evolve to an equilibrium where the revenue of market players is comparable, thus the fairness in TGC market is improved.     

An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation

In this paper, an innovative model of agent based simulation, based on Ant Colony Optimization (ACO) algorithm is proposed in order to compare three available strategies of clearing wholesale electricity markets, i.e. uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors of the power market are modeled as adaptive agents who learn how to bid strategically to optimize their profit through indirect interaction with other actors of the market. The proposed model is proper for bidding functions with high number of dimensions and enables modelers to avoid curse of dimensionality as dimension grows. Test systems are then used to study the behavior of each pricing rule under different degrees of competition and heterogeneity. Finally, the pricing rules are comprehensively compared using different economic criteria such as average cleared price, efficiency of allocation, and price volatility. Also, principle component analysis (PCA) is used to rank and select the best price rule. To the knowledge of the authors, this is the first study that uses ACO for assessing strategies of wholesale electricity market.     

Integrating Real Options Analysis with long-term electricity market models

In liberalized electricity markets, the investment postponement option is deemed decisive for understanding the addition of new generating capacity. Basically, it refers to the possibility for investors to postpone projects for a period while waiting for the arrival of new and better information about the market evolution. When such development involves major uncertainties, the generation business becomes riskier, and the investors' “wait-and-see” behavior might limit the timely addition of new power plants. In that sense, the literature provides solid empirical evidence about the occurrence of construction cycles in the deregulated electricity industry. However, the strategic flexibility inherent to the option to defer new power plants has not yet been rigorously incorporated to investment signals in existing market models. Therefore, this paper proposes a novel methodology to assess the long-term development of liberalized power markets based on a more realistic approach for valuing generation investments. The work is based on a stochastic dynamic market model, built upon System Dynamics simulation approach. The decision-making framework considers that the addition of new capacity is driven by the economic value of the strategic flexibility associated with deferring investments under uncertainties. Thus, the value of the postponement option is quantified in monetary terms through Real Options Analysis. Simulations confirm the cyclical behavior of the energy-only market in the long run, as suggested by the empirical evidence found in the literature. In addition, sensitivity analysis regarding some relevant exogenous variables depicts an even more fluctuating evolution of the capacity due to the combination of strong demand growth rates with large volatilities. Finally, the model validity is assessed through a formal procedure according to the scope of System Dynamics modeling approach.     

Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis

The energy transition (“Energiewende”) in Germany will result in a substantial transformation of the energy supply system. Virtual power plants are expected to be important components of the new intelligent energy infrastructure. They aggregate beside different types of distributed generation units also active consumers and storage technologies in order to integrate these in a profit-maximising, system-stabilising, and sustainable way. The assessment of the economic performance of virtual power plants requires a scenario-based and model-supported analysis. In this relation, future energy market conditions are simulated using the scenario methodology. Starting from the year 2015, three scenarios have been identified that illustrate alternative energy developments in Germany by 2030. Based on these scenarios, the additional revenues potential of the modeled virtual power plant is identified when compared to an independent and non-market-oriented operation mode of distributed energy resources. According to the model results, revenues of the VPP can increase by 11% up to 30% in the analyzed scenarios in 2030 due to the market-oriented operation mode. Nevertheless, the amount and composition vary depending on technology-specific subsidies, temporary nature of power demand and price structures in the energy market. Fluctuating renewable energies are expected to benefit from the market-oriented operation mode in the virtual power plant, especially through the EEG direct marketing. The selective and regulated shutdown of renewable energies in times of negative electricity prices may lead to further cost savings. The utilization of temporary price fluctuations in the spot market and the demand-oriented provision of control power offer high additional revenue potential for flexible controllable technologies such as battery storage, biomethane as well as combined heat and power units. Finally, the determination of the long-term profitability of a virtual power plant still requires a full-scale cost–benefit analysis. For this holistic approach, the model results provide a reliable scientific basis.     

Reactive power market management considering voltage control area reserve and system security

This paper presents a new algorithm to optimize reactive power procurement through commercial transactions considering system voltage security. The proposed algorithm minimizes reactive power provision and transmission loss costs in addition to maximizing system voltage security margin through a multiobjective function. In order to maintain the voltage profile of power system during sever contingencies or due to load uncertainty, all voltage control areas (VCA) of the system are detected and then optimal reactive power reserve is provided for each VCA during the market settlement. A four-stage multiobjective mathematical programming method is proposed to settle the reactive power market. The proposed algorithm has been applied on IEEE-RTS test system. The simulation results show the effectiveness of the proposed algorithm for reactive power market management.     

Examining market power in the European natural gas market

In this paper, we develop a mixed complementarity equilibrium model for the European natural gas market. This model has producers as Cournot players with conjectured supply functions relative to their rivals. As such, these producers can withhold production to increase downstream prices for greater profits. The other players are taken to be perfectly competitive and are combined with extensive pipeline, seasonal, and other data reflecting the current state of the market. Four market scenarios are run to analyze the extent of market power by these producers as well as the importance of pipeline and storage capacity.     

Transmission capacities and competition in Western European electricity market

The integration of national electricity markets into a single European one is expected to reduce the ability of dominant players to exercise market power. This paper investigates whether or not existing transmission capacities of cross-border interconnectors are sufficient to achieve this result and create vigorous competition in the market. A model with two decision levels is used. On the first level profit maximizing generators play Cournot game against each other. On the last level the system operator clears the market and determines flows in the network to maximize social welfare subject to a set of physical constraints. As each strategic generator anticipates her impact on equilibrium prices and congestion in the system, her optimization problem is subject to equilibrium constraints from the system operator's problem.The analysis demonstrates that interconnector capacities in Western Europe are insufficient for integration alone to reduce the exercise of market power. I compare several possible competition-enhancing policies: expansion of interconnectors and different scenarios of national markets’ restructuring. I show that although increase of line capacity is a useful tool to stimulate competition in an integrated market, it is not a substitute for the restructuring of large players.     

Stochastic reactive power market with volatility of wind power considering voltage security

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

Real-world market representation with agents

As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial and regulatory environments in which they operate. A new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation (ABMS) approach. The purpose of an ABMS model is not necessarily to predict the outcome of a system but to reveal and understand the complex and aggregate system behaviors that emerge from the interactions of the heterogeneous individual entities. Emergent behavior is a key feature of ABMS and is not easily inferred from the simple sum of the behavior of its components. By relying on both established engineering modeling techniques as well as advanced quantitative economic market principles, the ABMS approach is uniquely suited to addressing the strategic issues of interest to different market participants as well as those of market monitors and regulators.     

A complementarity model for the European natural gas market

In this paper, we present a detailed and comprehensive complementarity model for computing market equilibrium values in the European natural gas system. Market players include producers and their marketing arms which we call “traders”, pipeline and storage operators, marketers, LNG liquefiers, regasifiers, tankers, and three end-use consumption sectors. The economic behavior of producers, traders, pipeline and storage operators, liquefiers and regasifiers is modeled via optimization problems whose Karush–Kuhn–Tucker (KKT) optimality conditions in combination with market-clearing conditions form the complementarity system. The LNG tankers, marketers and consumption sectors are modeled implicitly via appropriate cost functions, aggregate demand curves, and ex post calculations, respectively. The model is run on several case studies that highlight its capabilities, including a simulation of a disruption of Russian supplies via Ukraine.     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.     

A fundamental analysis on the implementation and development of virtual natural gas hubs

The ongoing gas market liberalization in Europe has brought up a new competitive environment in which shippers (i.e., companies that are responsible for conveying gas from producers to consumers) must adapt their behavior to the changing conditions. The development of gas virtual hubs increases market interactions among shippers, but the oligopolistic market structure may give room for strategic behavior. The market is in addition segmented by type of costumer. Each shipper maximizes its profit by supplying gas to households, businesses and industries (conventional costumers), participating in the electricity market, trading in the global LNG spot market and interacting with the rest of shippers in a virtual hub. During the hub implementation and development, the following questions arise: How do shippers behave at the different levels of hub maturity? And, to what extent does the implementation of virtual hubs in entry-exit systems diminish the barriers to entry of new market players, provides more flexibility and fosters competition?With this aim, the decision-making process of the different shippers is simulated under different market structures, representing four stages of the market liberalization process at different levels of hub maturity. First, the proto-liberalization case includes the global LNG spot market which is represented as a perfectly competitive market, the electricity market which is represented as an oligopoly, and the conventional demand which is assumed to be captive (i.e., monopolized). Second, a hub is implemented, which provides transparency and reduces information costs by revealing the gas price. Third, switching rates are expected to grow as consumers have access to a transparent gas price; hence, the conventional demand is no longer considered as captive. Fourth, wholesale (procurement) and retail activities are unbundled, and a wholesale market is established where the retailers presumably buy gas from the shippers; thereby wholesale and retail activities acquire importance and market transactions (i.e., liquidity) increase. From the simulation and the analysis of the different market equilibria, the following conclusions emerge. First, with the introduction of the virtual hub, the marginal cost of all shippers reaches a unique value, i.e., the transparent gas hub price. Second, the aggregated profit of the shippers is increasing even when anticompetitive behavior is not explicitly represented, due to the flexibility gained by shippers with the hub. Accordingly, and third, the hub is a necessary, but not sufficient, condition to increase competition. The entry of new players is critical and discouraging market regulations or the anticompetitive behavior of a highly concentrated market may not facilitate it.     

An agent-based analysis of the German electricity market with transmission capacity constraints

While some agent-based models have been developed for analyzing the German electricity market, there has been little research done on the emerging issue of intra-German congestion and its effects on the bidding behavior of generator agents. Yet, studies of other markets have shown that transmission grid constraints considerably affect strategic behavior in electricity markets. In this paper, the implications of transmission constraints on power markets are analyzed for the case of Germany. Market splitting is applied in the case of congestion in the grid. For this purpose, the agent-based modeling of electricity systems (AMES) market package developed by Sun and Tesfatsion is modified to fit the German context, including a detailed representation of the German high-voltage grid and its interconnections. Implications of transmission constraints on prices and social welfare are analyzed for scenarios that include strategic behavior of market participants and high wind power generation. It can be shown that strategic behavior and transmission constraints are inter-related and may pose severe problems in the future German electricity market.     

Multiobjective environmental/techno-economic approach for strategic bidding in energy markets

This paper describes a method for developing optimal bidding strategy based on a bilevel optimization, considering suppliers’ emission of pollutants. The proposed methodology employs supply function equilibrium (SFE) model to represent the strategic behavior of each supplier. Locational marginal pricing mechanism is also assumed for settling the market and calculating the supplier profit. It is modeled as a bilevel optimization problem in which the upper-level subproblem maximizes individual supplier payoff and the lower-level subproblem solves the Independent System Operator’s market clearing problem. In this paper, the multiobjective optimal power flow is used to solve market clearing mechanism with supplier emission of pollutants, as extra objectives, subject to the transmission limits and supplier physical constraints. To illustrate the proposed approach under different conditions an IEEE 30-bus test system together with a number of case studies are used.     

Risk‐based optimal energy management of virtual power plant with uncertainties considering responsive loads

This paper proposes a stochastic scheduling model to determine optimal operation of generation and storage units of a virtual power plant (VPP) for participating in a joint energy and regulation service (RS) market under uncertainty. Beside electricity, the VPP provides required RSs according to the probability of delivery request in the electricity market. A new model for providing RS is introduced in which the dispatchable generation units are financially compensated with their readiness declarations and will be charged/paid for their real‐time down/up regulations. Besides, the VPP sets up incentive price‐quantity curves to benefit from the potential of demand side management in both energy and RS market. Within the model presented here, the VPP consists of two types of generation units: wind turbine and standby diesel generator; the latter is modeled by considering CO₂‐emission penalty costs. The given uncertainties are divided into two parts. Firstly, the uncertainties from the energy market price are simulated using information gap decision theory to evaluate the risk‐based resource scheduling for both risk‐taker and risk‐averse VPP. Other uncertainties affecting decision making such as wind turbine generation, load, regulation up/down calling probabilities, and regulation market prices are modeled via scenario trees. Three typical case studies are implemented to validate the performance and effectiveness of the proposed scheduling approach.     

A game theoretic model of the Northwestern European electricity market—market power and the environment

This paper develops a static computational game theoretic model. Illustrative results for the liberalising European electricity market are given to demonstrate the type of economic and environmental results that can be generated with the model. The model is empirically calibrated to eight Northwestern European countries, namely Belgium, Denmark, Finland, France, Germany, The Netherlands, Norway, and Sweden. Different market structures are compared, depending on the ability of firms to exercise market power, ranging from perfect competition without market power to strategic competition where large firms exercise market power. In addition, a market power reduction policy is studied where the near-monopolies in France and Belgium are demerged into smaller firms. To analyse environmental impacts, a fixed greenhouse gas emission reduction target is introduced under different market structures. The results indicate that the effects of liberalisation depend on the resulting market structure, but that a reduction in market power of large producers may be beneficial for both the consumer (i.e. lower prices) and the environment (i.e. lower greenhouse gas permit price and lower acidifying and smog emissions).     

Evaluating the economic cost of natural gas strategic storage restrictions

The European Commission wants to implement a single market for gas. One of the components of this market is a regulated provision for “security of supply” which consists of rules for the implementation and use of a given reserve stock of gas. We investigate the impact of this policy on the profitability of a storage operator, using data from Denmark and Italy. Keeping storage capacity constant, the costs of the strategic stock are around 20% of the value of the storage market for Denmark, and 16% for Italy. This cost is due to the inability to extract arbitrage profits from the captive stock. Furthermore, the strategic storage restriction induces behavior that would virtually never be replicated by a private storage operator in an unconstrained market, in particular in the first 6 months of the year when unconstrained firms empty their reservoirs much faster, suggesting the strategic restriction is unnecessarily distorting the market.     

Interaction among competitive producers in the electricity market: An iterative market model for the strategic management of thermal power plants

The liberalization of the electricity sector requires utilities to develop sound operation strategies for their power plants. In this paper, attention is focused on the problem of optimizing the management of the thermal power plants belonging to a strategic producer that competes with other strategic companies and a set of smaller non-strategic ones in the day-ahead market. The market model suggested here determines an equilibrium condition over the selected period of analysis, in which no producer can increase profits by changing its supply offers given all rivals’ bids. Power plants technical and operating constraints are considered. An iterative procedure, based on the dynamic programming, is used to find the optimum production plans of each producer. Some combinations of power plants and number of producers are analyzed, to simulate for instance the decommissioning of old expensive power plants, the installation of new more efficient capacity, the severance of large dominant producers into smaller utilities, the access of new producers to the market. Their effect on power plants management, market equilibrium, electricity quantities traded and prices is discussed.     

Competitiveness assessment of Iran's restructured electricity market

Iran's electricity market was restructured mostly to enhance its production efficiency. Nonetheless, the experience of liberalized electricity markets indicates that market establishment would be insufficient to produce competitive results. To determine whether the restructured market is moving toward competition, this paper assesses production efficiency in Iran's electricity market during high demand periods in 2006 as the first year of the restructured market's performance, and in 2012 as the last year with available data. We compared counterfactual benchmark outcomes to the actual dispatches to determine the production efficiency. Moreover, Iran's power market is a discriminatory, day-ahead auction; therefore, we considered the market design in the competitive benchmark. We found that the production was inefficient in both 2006 and 2012. More importantly, the production in the market is becoming increasingly inefficient over time due to exercising market power and distortion of the market's production by strategic firms. Not only were strategic firms distorting the market's production, but they were also learning to exercise more non-competitive behavior because these firms distorted the production in 2012 eight times more than they did in 2006.     

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.