Vladimir Koritarov

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.

Multi-agent power market simulation using EMCAS

Countries around the world continue to restructure their electricity markets and open them up to competition and private investors in pursuit of economic efficiency and new capital investment. However, the recent volatility exhibited by many restructured power markets, in combination with several prominent market failures, have highlighted the need for a better understanding of the complex interactions between the various market participants and the emerging overall market behavior. Advanced modeling approaches are needed that simulate the behavior of electricity markets over time and model how market participants may act and react to changes in the underlying economic, financial, and regulatory environments. This is particularly useful for developing sound market rules that will allow these markets to function properly. A new and promising approach is to model electricity markets as complex adaptive systems using an agent-based modeling and simulation approach, such as is implemented in the electricity market complex adaptive system (EMCAS) software. EMCAS provides an agent-based framework to capture and investigate the complex interactions between the physical infrastructures and the economic behavior of market participants that are a trademark of the newly emerging markets. This paper describes the EMCAS agents, their interactions, the unique insights obtained from agent-based models, and discusses current model applications in several U.S., Asian, and European markets.

Incorporating environmental concerns into electric system expansion planning using a multi–criteria decision support system

A methodology is described for incorporating environmental externalities into electric system expansion planning. It combines a conventional least–cost optimisation tool, an environmental model, and a multi–criteria interval decision analysis system. The methodology can be used to analyse trade–offs between technical, economic, and environmental concerns. A case study in support of an international lending decision is used to analyse eleven power–sector expansion scenarios in terms of total system cost and environmental residuals. The scenarios are then compared by using a decision analysis tool. A brief comparison of interval and traditional decision analysis methodologies is also provided.

An agent-based approach to modeling interactions between emission market and electricity market

An agent-based approach is proposed in this paper to analyze interactions between the emission and electricity markets. A cap-and-trade system is assumed to be in place to regulate emissions from power generation. Generation companies are modeled as adaptive learning agents that can bid strategically into the electricity market by Q-learning algorithm. These companies also participate in allowances trading in the emission market by adjusting their own allowances positions. In the simulation, generation companies can value their generation capacity and available allowances to maximize their profits. The results show that the initial allowance will influence the operation of power producers and that some generation companies may need to raise bid prices to recover their expenses for buying additional allowances. The results also reveal that in some cases generation companies may not increase profits by participating in both markets compared with bidding in the electricity market alone. This modeling framework can help design a sound emission market by simulating market scenarios with different policies, such as allowances caps. It can be also used to investigate the operation strategies for generation companies in such an environment.

Market power analysis in the EEX electricity market: An agent-based simulation approach

In this paper, an agent-based modeling and simulation (ABMS) approach is used to model the German wholesale electricity market. The spot market prices in the European Energy Exchange (EEX) are studied as the wholesale market prices. Each participant in the market is modeled as an individual rationality-bounded agent whose objective is to maximize its own profit. By simulating the market clearing process, the interaction among agents is captured. The market clearing price formed by agentspsila production cost bidding is regarded as the reference marginal cost. The gap between the marginal cost and the real market price is measured as an indicator of possible market power exertion. Various bidding strategies such as physical withholding and economic withholding can be simulated to represent strategic bidding behaviors of the market participants. The preliminary simulation results show that some generation companies (GenCos) are in the position of exerting market power by strategic bidding.

Modeling hydro power plants in deregulated electricity markets: Integration and application of EMCAS and VALORAGUA

In this paper, we present details of integrating an agent-based model, Electricity Market Complex Adaptive System (EMCAS) with a hydro-thermal coordination model, VALORAGUA. EMCAS provides a framework for simulating deregulated markets with flexible regulatory structure along with bidding strategies for supply offers and demand bids. VALORAGUA provides longer-term operation plans by optimizing hydro and thermal power plant operation for the entire year. In addition, EMCAS uses the price forecasts and weekly hydro schedules from VALORAGUA to provide intra-week hydro plant optimization for hourly supply offers. The integrated model is then applied to the Iberian electricity market which includes about 111 thermal plants and 38 hydro power plants. We then analyze the impact of hydro plant supply offers on the market prices and ways to minimize the Gencospsila exposure to price risk.

Agent-based modeling of interaction between commercial building stocks and power grid

This paper describes a preliminary study on simulating commercial buildings modeled as consumer agents that interact with the power grid. A simple hourly bottom-up building energy model is developed with respect to climate conditions and building design and operation. This model is used to simulate different types of commercial buildings as agents and to derive the hourly load profile of the entire building stock at the city/regional level. By updating building operating parameters in this bottom-up model according to different occupant control strategies under real-time electricity pricing, the total electricity demand of the building stock can be estimated; this will, in turn, affect the electricity market. Two test cases are modeled to estimate the commercial building stock demand response and its impact on the regional electricity market.

Dynamic modeling of adjustable-speed pumped storage hydropower plant

Hydropower is the largest producer of renewable energy in the U.S. More than 60% of the total renewable generation comes from hydropower. There is also approximately 22 GW of pumped storage hydropower (PSH). Conventional PSH uses a synchronous generator, and thus the rotational speed is constant at synchronous speed. This work details a hydrodynamic model and generator/power converter dynamic model. The optimization of the hydrodynamic model is executed by the hydro-turbine controller, and the electrical output real/reactive power is controlled by the power converter. All essential controllers to perform grid-interface functions and provide ancillary services are included in the model.

Quantifying the operational benefits of conventional and advanced pumped storage hydro on reliability and efficiency

Pumped storage hydro (PSH) plants have significant potential in providing reliability and efficiency benefits in future electric power systems. New PSH technologies, like adjustable-speed PSH, have also been introduced and can present further benefits. An understanding of these benefits on systems with high penetrations of variable generation (VG) is a primary focus. This paper will demonstrate and quantify some of the reliability and efficiency benefits afforded by pumped storage hydro plants utilizing the Flexible Energy Scheduling Tool for Integrating Variable generation (FESTIV), an integrated power system operations tool which evaluates both reliability and production costs. A description about the FESTIV tool and how it simulates PSH operations at multiple timescales will be given. Impacts of PSH on area control error, production costs, and system operation are quantified on a high VG scenario in the Balancing Area of Northern California. We also perform a study on how advanced PSH can provide a fast form of regulation to improve reliability and potentially reduce costs.

Cap and Trade Modeling in Electricity Markets Using an Agent-Based Approach

An agent-based approach is proposed in this book chapter to analyze interactions between the emission and electricity markets. A cap-and-trade system is assumed to be in place to regulate emissions from power generation. Generation companies are modeled as adaptive learning agents that can bid strategically into the electricity market by a Q-learning algorithm. These companies also participate in allowances trading in the emission market by adjusting their own allowances positions. In the simulation, generation companies can value their generation capacity and available allowances to maximize their profits. Three different trading strategies are modeled to mimic the decision-making process of generation companies. This modeling framework can help design a sound emission market by simulating market scenarios with different policies. It can also be used to investigate the operation strategies for generation companies in such an environment.