Dynamic assessment of capacity investment in electricity market considering complementary capacity mechanisms

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market. Investment decisions are fundamentally based on total revenues gained by investors. In most electricity markets, the complementary mechanisms are designed to ensure a desired level of reliability while covering investment costs of the suppliers. In such an environment, investment decisions are highly sensitive to expectation of price signals in both of energy market and capacity mechanisms. In this work, the system dynamics concepts are used to model the structural characteristics of electricity market such as, long-term firms’ behavior and relationships between variables, feedbacks, and time delays by appropriately bundling the energy market and capacity mechanisms. The market oriented capacity price as well as non-competitive capacity payments and a proposed hybrid capacity mechanism are linked with the energy market in the model. Such a decision model enables both the generation companies and the regulators gaining perfect insights into the possible consequences of different decisions they make under different policies and market conditions. In order to examine the performance of the electricity market with different capacity mechanisms, a case study is presented which exhibits the effectiveness of the proposed model.     

A decision support tool for the analysis of pricing,investment and regulatory processes in a decentralized electricity market

After the liberalization of the electricity generation industry, capacity expansion decisions are made by multiple self-oriented power companies. Unlike the centralized environment, decision-making of market participants is now guided by price signal feedbacks and by an imperfect foresight of the future market conditions (and competitor actions) that they will face. In such an environment, decision makers need to better understand long-term dynamics of the supply and demand sides of the power market. In this study, a system dynamics model is developed, to better understand and analyze the decentralized and competitive electricity market dynamics in the long run. The developed simulation model oversees a 20-year planning horizon; it includes a demand module, a capacity expansion module, a power generation module, an accounting and finance module, various competitors, a regulatory body and a bidding mechanism. Many features, singularities and tools of decentralized markets, such as; capacity withholding, enforced divestment, long-term contracts, price-elastic demands, incentives/disincentives, are also incorporated into the model. Public regulators and power companies are potential users of the model, for learning and decision support in policy design and strategic planning. Results of scenario analysis are presented to illustrate potential use of the model.     

How does market power affect the impact of large scale wind investment in 'energy only' wholesale electricity markets?

In the short run, it is well known that increasing wind penetration is likely to reduce spot market electricity prices due to the merit order effect. The long run effect is less clear because there will be a change in new capacity investment in response to the wind penetration. In this paper we examine the interaction between capacity investment, wind penetration and market power by first using a least-cost generation expansion model to simulate capacity investment with increasing amounts of wind generation, and then using a computer agent-based model to predict electricity prices in the presence of market power. We find the degree to which firms are able to exercise market power depends critically on the ratio of capacity to peak demand. For our preferred long run generation scenario we show market power increases for some periods as wind penetration increases however the merit order counteracts this with the results that prices overall remain flat. Returns to peakers increase significantly as wind penetration increases. The market power in turn leads to inefficient dispatch which is exacerbated with large amounts of wind generation.     

Analysis of imperfect competition in natural gas supply contracts for electric power generation: A closed-loop approach

The supply of natural gas is generally based on contracts that are signed prior to the use of this fuel for power generation. Scarcity of natural gas in systems where a share of electricity demand is supplied with gas turbines does not necessarily imply demand rationing, because most gas turbines can still operate with diesel when natural gas is not available. However, scarcity conditions can lead to electricity price spikes, with welfare effects for consumers and generation firms. We develop a closed-loop equilibrium model to evaluate if generation firms have incentives to contract or import the socially-optimal volumes of natural gas to generate electricity. We consider a perfectly-competitive electricity market, where all firms act as price-takers in the short term, but assume that only a small number of firms own gas turbines and procure natural gas from, for instance, foreign suppliers in liquefied form. We illustrate an application of our model using a network reduction of the electric power system in Chile, considering two strategic firms that make annual decisions about natural gas imports in discrete quantities. We also assume that strategic firms compete in the electricity market with a set of competitive firms do not make strategic decisions about natural gas imports (i.e., a competitive fringe). Our results indicate that strategic firms could have incentives to sign natural gas contracts for volumes that are much lower than the socially-optimal ones, which leads to supernormal profits for these firms in the electricity market. Yet, this effect is rather sensitive to the price of natural gas. A high price of natural gas eliminates the incentives of generation firms to exercise market power through natural gas contracts.     

A real options model for renewable energy investment with application to solar photovoltaic power generation in China

This paper proposes a real options model for evaluating renewable energy investment by considering uncertain factors such as CO₂ price, non-renewable energy cost, investment cost and market price of electricity. A phase-out mechanism is built into the model to reflect the long-term changes of subsidy policy. We apply the proposed model to empirically evaluate the investment value and optimal timing for solar photovoltaic power generation in China. Our empirical results show that the current investment environment in China may not be able to attract immediate investment, while the development of carbon market helps advance the optimal investment time. A sensitivity analysis is conducted to investigate the dynamics of investment value and optimal timing under the changes of unit generating capacity, subsidy level, market price of electricity, CO₂ price and investment cost. It is found that the high investment cost and the volatility of electricity and CO₂ prices, are not conducive to attract immediate investment. Instead, increasing the level of subsidy, promoting technological progress and maintaining the stability of market are useful to stimulate investment.     

Optimal investment strategies in decentralized renewable power generation under uncertainty

This paper presents a method for evaluating investments in decentralized renewable power generation under price un certainty. The analysis is applicable for a client with an electricity load and a renewable resource that can be utilized for power generation. The investor has a deferrable opportunity to invest in one local power generating unit, with the objective to maximize the profits from the opportunity. Renewable electricity generation can serve local load when generation and load coincide in time, and surplus power can be exported to the grid. The problem is to find the price intervals and the capacity of the generator at which to invest. Results from a case with wind power generation for an office building suggests it is optimal to wait for higher prices than the net present value break-even price under price uncertainty, and that capacity choice can depend on the current market price and the price volatility. With low price volatility there can be more than one investment price interval for different units with intermediate waiting regions between them. High price volatility increases the value of the investment opportunity, and therefore makes it more attractive to postpone investment until larger units are profitable.     

Efficient pricing and investment in electricity markets with intermittent resources

Facing growing technological and environmental challenges, the electricity industry needs effective pricing mechanism to promote efficient risk management and investment decisions. In a restructured electricity market with competitive wholesale prices and traditionally regulated retail rates, however, there are technical and institutional barriers that prevent dynamic pricing with price responsive demand. In regions with limited energy storage capacity, intermittent renewable resources present special challenges. This could adversely affect the effectiveness of public policies causing inefficient investments in energy technologies. In this paper, we present an updated economic model of pricing and investment in restructured electricity market and use the model in a simulation study for an initial assessment of renewable energy strategy and alternative pricing mechanisms. A key objective of the study is to shed light on the policy issues so that effective decisions can be made to improve efficiency.     

A combined modeling approach for wind power feed-in and electricity spot prices

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series.     

The large-scale integration of wind generation: Impacts on price,reliability and dispatchable conventional suppliers

This work examines the effects of large-scale integration of wind powered electricity generation in a deregulated energy-only market on loads (in terms of electricity prices and supply reliability) and dispatchable conventional power suppliers. Hourly models of wind generation time series, load and resultant residual demand are created. From these a non-chronological residual demand duration curve is developed that is combined with a probabilistic model of dispatchable conventional generator availability, a model of an energy-only market with a price cap, and a model of generator costs and dispatch behavior. A number of simulations are performed to evaluate the effect on electricity prices, overall reliability of supply, the ability of a dominant supplier acting strategically to profitably withhold supplies, and the fixed cost recovery of dispatchable conventional power suppliers at different levels of wind generation penetration. Medium and long term responses of the market and/or regulator in the long term are discussed.     

Investment incentives in the Korean electricity market

This paper develops a model-based analysis of the effects of various capacity incentive systems on new investment in the Korean electricity market. The restructuring process in Korea allocated power generation to six firms, competing within a wholesale market, albeit strictly on a cost basis. Because of this cost-based pool, capacity payments were also introduced to encourage new investment. However, it is an open question whether the current fixed capacity payment scheme is enough to secure resource adequacy, and consideration is being given to alternative mechanisms such as the use of LOLP. Using a detailed market simulation model, based on system dynamics, we compare these approaches in terms of how they may influence the investors’ decisions and thereby determine the system reserve margin. The simulation results suggest that there may be serious problems in staying with the current fixed capacity payments in order to achieve resource adequacy. In contrast, an LOLP-based capacity mechanism may, in the longer term, increase the reserve margin compared with a fixed capacity payment. More generally, this paper indicates how crucial the effective modeling of the investment behavior of the independent power producers is for adequate policy support, even if they only constitute a fringe in a substantially centrally influenced market.     

High penetration wind generation impacts on spot prices in the Australian national electricity market

This paper explores wind power integration issues for the South Australian (SA) region of the Australian National Electricity Market (NEM) by assessing the interaction of regional wind generation, electricity demand and spot prices over 2 recent years of market operation. SA's wind energy penetration has recently surpassed 20% and it has only a limited interconnection with other regions of the NEM. As such, it represents an interesting example of high wind penetration in a gross wholesale pool market electricity industry. Our findings suggest that while electricity demand continues to have the greatest influence on spot prices in SA, wind generation levels have become a significant secondary influence, and there is an inverse relationship between wind generation and price. No clear relationship between wind generation and demand has been identified although some periods of extremely high demand may coincide with lower wind generation. Periods of high wind output are associated with generally lower market prices, and also appear to contribute to extreme negative price events. The results highlight the importance of electricity market and renewable policy design in facilitating economically efficient high wind penetrations.     

Market power in the European electricity market—The impacts of dry weather and additional transmission capacity

This paper uses a static computational game theoretic model of a fully opened European electricity market and can take strategic interaction among electricity-producing firms into account. The model is run for a number of scenarios: first, in the baseline under perfect competition, the prices differ due to the presence of various generation technologies and a limited ability to exchange electricity among countries. In addition, when large firms exercise market power, the model runs indicate that prices are the highest in countries where the number of firms is low. Second, dry weather would increase the prices in the hydro-rich Nordic countries followed by the Alpine countries. The price response would be about 20% higher with market power. Third, more transmission capacity would lower the prices in countries with high prices and it also reduces the impact of market power. Hence, more transmission capacity can improve market competitiveness.     

Generation expansion planning (GEP) - A long-term approach using system dynamics and genetic algorithms (GAs)

This paper presents a model to solve the Generation Expansion Planning (GEP), problem in competitive electricity markets. The developed approach recognizes the presence of several generation agents aiming at maximizing their profits and that the planning environment is influenced by uncertainties affecting the demand, fuel prices, investment and maintenance costs and the electricity price. Several of these variables have interrelations between them turning it important to develop an approach that adequately captures the long-run behavior of electricity markets. In the developed approach we used System Dynamics to capture this behavior and to characterize the evolution of electricity prices and of the demand. Using this information, generation agents can then prepare their individual expansion plans. The resulting individual optimization problems have a mixed integer nature, justifying the use of Genetic Algorithms (GAs). Once individual plans are obtained, they are input once again on the System Dynamics model to update the evolution of the price, of the demand and of the capacity factors. This defines a feedback mechanism between the individual expansion planning problems and the long-term System Dynamics model. This approach can be used by a generation agent to build a robust expansion plan in the sense it can simulate different reactions of the other competitors and also by regulatory or state agencies to investigate the impact of regulatory decisions on the evolution of the generation system. Finally, the paper includes a Case Study to illustrate the use and the results of this approach.     

Incentives of carbon dioxide regulation for investment in low-carbon electricity technologies in Texas

This paper compares the incentives a carbon dioxide emissions price creates for investment in low carbon dioxide-emitting technologies in the electricity sector. We consider the extent to which operational differences across generation technologies – particularly, nuclear, wind and solar photovoltaic – create differences in the incentives for new investment, which is measured by the operating profits of a potential entrant. First, astylized model of an electricity system demonstrates that the composition of the existing generation system may cause electricity prices to increase by different amounts over time when a carbon dioxide price is imposed. Differences in operation across technologies therefore translate to differences in the operating profits of a potential entrant. Then, a detailed simulation model is used to consider a hypothetical carbon dioxide price of $10–$50 per metric ton for the Electric Reliability Council of Texas (ERCOT) market. The simulations show that, for the range of prices considered, the increase in electricity prices is positively correlated with output from a typical wind unit, but the correlation is much weaker for nuclear and photovoltaic. Consequently, a carbon dioxide price creates much stronger investment incentives for wind than for nuclear or photovoltaic technologies in the Texas market.     

Modeling long-term dynamics of electricity markets

In the last decade, many countries have restructured their electricity industries by introducing competition in their power generation sectors. Although some restructuring has been regarded as successful, the short experience accumulated with liberalized power markets does not allow making any founded assertion about their long-term behavior. Long-term prices and long-term supply reliability are now center of interest. This concerns firms considering investments in generation capacity and regulatory authorities interested in assuring the long-term supply adequacy and the stability of power markets. In order to gain significant insight into the long-term behavior of liberalized power markets, in this paper, a simulation model based on system dynamics is proposed and the underlying mathematical formulations extensively discussed. Unlike classical market models based on the assumption that market outcomes replicate the results of a centrally made optimization, the approach presented here focuses on replicating the system structure of power markets and the logic of relationships among system components in order to derive its dynamical response. The simulations suggest that there might be serious problems to adjust early enough the generation capacity necessary to maintain stable reserve margins, and consequently, stable long-term price levels. Because of feedback loops embedded in the structure of power markets and the existence of some time lags, the long-term market development might exhibit a quite volatile behavior. By varying some exogenous inputs, a sensitivity analysis is carried out to assess the influence of these factors on the long-run market dynamics.     

Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market

In 2002, China began to inspire restructuring of the electric power sector to improve its performance. Especially, with the rapid increase of electricity demand in China, there is a need for non-utility generation investment that cannot be met by government finance alone. However, a first prerequisite is that regulators and decision-makers (DMs) should carefully consider how to balance the need to attract private investment against the policy objectives of minimizing monopoly power and fostering competitive markets. So in the interim term of electricity market, a decentralized decision-making process should eventually replace the centralized generation capacity expansion planning. In this paper, firstly, on the basis of the current situation, a model for evaluating generation projects by comprehensive utilization of fuzzy appraisal and analytic hierarchy process (AHP) is developed. Secondly, a case study of generation project evaluation in China is presented to illustrate the effectiveness of the model in selecting optimal generation projects and attracting private investors. In the case study, with considerations of attracting adequate private investment and promoting energy conservation in China, five most promising policy instruments selected as evaluation factors include project duration, project costs, predicted on-grid price level, environmental protection, enterprise credit grading and performance. Finally, a comprehensive framework that enables the DM to have better concentration and to make more sound decisions by combining the model proposed with modern computer science is designed.     

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

The North Atlantic Oscillation (NAO) is a large-scale atmospheric circulation pattern driving climate variability in north-western Europe. As the deployment of wind-powered generation expands on electricity networks across Europe, the impacts of the NAO on the electricity system will be amplified. This study assesses the impact of the NAO, via wind-power generation, on the electricity market considering thermal generation costs, wholesale electricity prices and wind generation subsidies. A Monte Carlo approach is used to model NAO phases and generate hourly wind speed time-series data, electricity demand and fuel input data. A least-cost unit commitment and economic dispatch model is used to simulate an island electricity system, modelled on the all-island Irish electricity system. The impact of the NAO obviously depends on the level of wind capacity within an electricity system. Our results indicate that on average a switch from negative to positive NAO phase can reduce thermal generation costs by up to 8%, reduce wholesale electricity prices by as much as €1.5/MWh, and increase wind power generators' revenue by 12%.     

Short-term optimal wind power generation capacity in liberalized electricity markets

Mainly because of environmental concerns and fuel price uncertainties, considerable amounts of wind-based generation capacity are being added to some deregulated power systems. The rapid wind development registered in some countries has essentially been driven by strong subsidizing programs. Since wind investments are commonly isolated from market signals, installed wind capacity can be higher than optimal, leading to distortions of the power prices with a consequent loss of social welfare. In this work, the influence of wind generation on power prices in the framework of a liberalized electricity market has been assessed by means of stochastic simulation techniques. The developed methodology allows investigating the maximal wind capacity that would be profitably deployed if wind investments were subject to market conditions only. For this purpose, stochastic variables determining power prices are accurately modeled. A test system resembling the size and characteristics of the German power system has been selected for this study. The expected value of the optimal, short-term wind capacity is evaluated for a considerable number of random realizations of power prices. The impact of dispersing the wind capacity over statistical independent wind sites has also been evaluated. The simulation results reveal that fuel prices, installation and financing costs of wind investments are very influential parameters on the maximal wind capacity that might be accommodated in a market-based manner.     

Do Intermittent Renewables Threaten the Electricity Supply Security?

Around the globe, intermittent renewable energies in the form of wind and solar power are on the rise. Their subsidization can be seen as a market intervention, which may deter optimal investment. Thus, this study tests the effect of renewable energies on investment in conventional electricity generation technologies. We estimate a dynamic investment model for 14 European economies for the period 2004–2016 and find a non-negligible negative impact of intermittent renewables on investment in peak-load capacity (mainly gas), while base-load (particularly coal) plants are unaffected. However, the production flexibility of gas-fired plants represents a particularly vital function to balance the supply intermittency of wind and solar. Thus, dispatchable conventional power plants are still necessary to back the system under scarcity events, such as unfavorable weather conditions during high electricity demand. Policymakers should be aware of the adverse effects of RES on investment in peak-load plants and may consider a redesign of the current system, for example by introducing capacity markets.     

Private and social benefits of a pumped hydro energy storage with increasing amount of wind power

In this paper, we calculate the long-term profitability of a pumped hydro energy storage (PHES) plant that is planned to be built in an old mine. We model the optimal PHES operation for several scenarios with different wind power penetration levels. Our modelling approach first involves estimating wholesale electricity prices for the day-ahead, intraday and balancing market as a function of wind power penetration. The estimated price profiles are implemented in a dynamic programming model, where the PHES plant maximises its balancing market revenue given the optimal commitment in the day-ahead market. We show that increasing the wind penetration changes the optimal PHES operation and increases the PHES profits. Additionally, we quantify how the costs of wind power balancing are affected by the PHES investment. Policy implications are drawn based on the estimated private and social benefits from the investment.