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Abstract: One of the fundamental requirements of a power system, especially in a small isolated power system (SIPS), is secure and reliable energy supply. In this paper, a novel Energy-Based System Well-being Analysis framework (EBSWBA) is presented to assess the reliability of systems containing renewable energy sources (RES), fossil-fueled sources and electricity storage. The framework combines the advantages of the well-known System Well-being Analysis (SWBA) and Expected Energy Not-Served (EENS) approaches, thus overcoming the weaknesses of the SWBA by considering all key factors that influence the system adequacy. Furthermore, two techniques are proposed for calculation of the EBSWBA indices: the Monte-Carlo Simulation (MCS) adopted for the EBSWBA framework, and the Contingency Enumeration for Small Isolated Power Systems (CESIPS) technique, which is derived from the Contingency Enumeration (CE) methodology. These techniques enable formation of probability distributions of the indices. The comparison of the results of classical SWBA and EBSWBA when applied to different SIPS architectures, operating schemes and load profiles demonstrates the superiority of EBSWBA over SWBA in terms of rendered information about the system state as well as in terms of computational efficiency, especially when the CESIPS technique is used for calculation of the EBSWBA indices. [Copyright &y& Elsevier]

The required rate of return (RROR) is a key input in the calculation of levelized cost of electricity, investment decisions and in policy making. The methodology proposed in this paper determines an adequate RROR for grid-connected photovoltaic (GCPV) systems. A calculation of RROR under market conditions has been carried out for four Eurozone countries as well as the assessment of the impact of feed-in-tariff (FIT) support schemes on risk reduction. Moreover, a formula has been proposed to quickly calculate the RROR for GCPV systems based on country risk, risk-free rate and FIT period length if relevant. The methodology can be further applied to other renewable energy sources and can be used to evaluate different support schemes. [ABSTRACT FROM AUTHOR]

Market simulators based on agent-based modeling techniques are frequently used for electricity market analyses. However, the majority of such analyses focus on the electricity markets bidding strategies on generation-side rather than on the demand-side. Meanwhile, the behavior of the demand-side in the system reserve provision has been less investigated. This paper presents a novel system reserve provision agent which is incorporated into a stochastic market optimization problem. The agent for the system reserve provision uses the SA-Q-learning algorithm to learn how much system reserve to offer at different times, while seeking to increase the ratio between their economic costs and benefits. The agent and its learning process are described in detail and are tested on the IEEE Reliability test system. It has been shown that incorporating the demand-side market strategies using the proposed agent improves the performance and the economic outcome for the consumers. [ABSTRACT FROM AUTHOR]

Abstract: The electricity retail business has always been exposed to a significant risk due to a very volatile nature of the wholesale electricity market. Recently, this trend has been accelerating, as electricity-forward price can rise or fall during one day by an amount comparable to the retailer’s margin. As a result, the risk that arises from sales contract offer maturity can significantly reduce or even neutralize the retailer’s profit. To develop a mitigation strategy for this risk, a retailer clearly needs a model for a detailed risk analysis. Many attempts have been made to analyze several risk issues of an electricity retail company, but so far none has explored and evaluated the sales contract offer maturity risk. This paper presents a fundamental model for evaluating this risk and a methodology for its use, and compares its analytical performance with traditional techniques obtained from the option-pricing theory. A realistic example illustrates the use and the benefits of the proposed model in risk analysis of the electricity retail business. [Copyright &y& Elsevier]

Abstract: Following advances in control and information technology, consumers are able to provide system reserve through flexible demand. In this way, consumers could maximise their benefit from electricity markets. This paper presents a new method for calculating the demand reserve offer function, where the costs and benefits that consumers derive from their participation in the reserve market are considered. A new stochastic modelling approach to demand-side reserve provision in co-optimised electricity and reserve markets with demand response included as a reserve provider is also presented. The stochastic model includes demand flexibility in order to realistically model the motivation and ability of the demand-side as a reserve provider. The proposed stochastic market model is formulated as a linear two-stage mixed-integer stochastic program, and was successfully tested using the IEEE Reliability Test System. We compare the results for the proposed method with those obtained using constant demand reserve offer functions. The results show that for the proposed demand reserve offer function, the optimal point, where the difference between consumer reserve cost and benefit is minimal, can be determined using the proposed method. The impact of demand flexibility on scheduling reserve, costs, benefits that demand derives from participating in the reserve market and energy payback effect were also examined. The results show that demand elasticity has a great influence on demand-side reserve provision and that sufficiently flexible demand is able to successfully compete with generators in providing system reserve. Finally, demand-side reserve provision leads to more reliable power system operation, due to less involuntary load curtailment and lower operating costs. [Copyright &y& Elsevier]