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Successful trading in electricity markets relies on the market actor's ability to accurately forecast the electricity price. The fundamental electricity price models use market information, provided by various price drivers, including the residual that contains a risk premium. In the past, researchers investigating risk premium focused primarily on daily spot price levels, ignoring the intraday information hindering the accurate risk premium determination. This paper presents a new KGB Method for modelling of risk premium, based on “ex-ante” approach focused on a yearly product. The method involves a novel KGB Model and its linearized formulation, the KGB Linear Model, which enables capturing the influence of renewable energy sources on risk premium. The four key drivers of the KGB Linear Model were used providing an insight into the influence of RES generation on risk premium evolution. The method was tested on historical data from the German electricity market. The results for the 2010–2014 period reveal overall influence of PV production share on risk premium is greater than that of wind production share, both increasing the risk premium due to their variability and uncertainty. Using the KGB Method, market actors can forecast risk premium using information readily available to them. [ABSTRACT FROM AUTHOR]

Hydropower contributes to a higher share of renewables but is also in conflict with environmental legislation aiming to protect natural habitats, wildlife and endangered species. Analysing two neighbouring European countries with similar topographical and hydromporphological characteristics, Austria and Slovenia, allows assessing to what extent a policies’ national context steers possible policy conflicts of EU legislation. The paper shows that hydropower expansion in examined countries is strongly shaped by factors such as financial concerns of investors, diverging opinions of involved stakeholders, available hydro potential and the role of hydropower in national energy strategies. Addressing issues that lead to a lower effectiveness of national policies already at the level of EU policy design or engaging countries to broadening their national energy portfolio by considering emerging technologies such as PV and wind will enable future energy and climate strategies that are more consistent with other environmental goals. [ABSTRACT FROM AUTHOR]

With the integration of distributed renewable energy sources (DRES) and active demand (e.g., units providing demand response, DR) in the distribution grid, the importance of monitoring the network conditions, managing the line congestions and observing the voltage levels is increasing. The distribution system operator (DSO) needs a mechanism, such as the traffic light system, to screen and approve the proposed operation schedules of the flexible active resources in the distribution grid. Their aggregated control will require the aggregators to employ advanced scheduling algorithms. The DR scheduling algorithms can be set to pursue various goals, for example, maximization of profit or cost reduction, grid support, or provision of the ancillary services. In the paper, we present a new DR scheduling approach suitable for the aggregation agent using approximate Q‐learning (AQL) algorithm scheduling. We present the AQL algorithm and the associated assumptions used in simulations on a real‐world low‐voltage (LV) grid model, comparing the AQL approach results to those of the economic scheduling and the energy scheduling approaches. Our assumption was that the AQL approach could outperform the energy or economic approaches as the AQL agent would be able to learn to avoid the scheduling penalties. The results of our research show that the aggregator agent using the economic approach shows the best economic performance, but causes the most schedule violations. The energy scheduling approach improves the network voltage profile but lowers the aggregator's profit. The AQL approach results in the agent's economic performance between the former two approaches with minimal schedule violations, confirming our research hypothesis. This article is categorized under: Concentrating Solar Power > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Efficiency > Economics and Policy [ABSTRACT FROM AUTHOR]

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]

An important indicator of supply and demand uncertainty on electricity markets is risk premium (RP), an integral part of electricity price. The level of RP is describing the risk the market actors expect in the future due to the market uncertainties. With the electricity market price behavior rapidly changing, the rising market supply uncertainty is increasing the volatility of RP. To better understand this behavior, power market participants need new models that will efficiently use the information available in the processes driving the electricity price and RP and to explain the influence of RES uncertainties on the future RP and electricity prices. A decade ago, researchers investigating RP focused primarily on the uncertainties arising from consumption forecast and generation outages. RES share in generation mix was small and its influence on uncertainty was negligible, leading to much lower volatilities of RP than today. With the increasing influence of RES, typically exhibiting variability on a sub‐hourly level, traditional models using daily electricity price to calculate RP were becoming inadequate. In this dynamic period of electricity market transformation, this paper highlights the importance of RP signals to market actors. A stochastic method for RP calculation is discussed with the associated RP model, driven by the intraday dynamics. An example of the RP signal is presented on historical price data from the German electricity market, highlighting uncertainty pattern developed over the years. With such an approach, the market actors can adjust their trading strategies thus mitigating their market risk exposure. This article is categorized under:Energy Systems Economics > Economics and PolicyEnergy Efficiency > Economics and PolicyEnergy Systems Analysis > Economics and Policy [ABSTRACT FROM AUTHOR]

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]