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We study the day-ahead electricity market clearing problem under the prevailing market design in the European electricity markets. We revisit the Benders decomposition algorithm that has been used to solve this problem. We develop new valid inequalities that substantially improve the performance of the algorithm. We generate instances that mimic the characteristics of past bids of the Turkish day-ahead electricity market and conduct experiments. We use two leading mixed-integer programming solvers, IBM ILOG Cplex and Gurobi, in order to assess the impact of employed solver on the algorithm performance. We compare the performances of our algorithm, the primal-dual algorithm, and the Benders decomposition algorithm using the existing cuts from the literature. The extensive experiments we conduct demonstrate that the price-based cuts we develop improve the performance of the Benders decomposition algorithm and outperform the primal-dual algorithm.

Large greenhouse gas reductions are possible with a fully decarbonised grid and electric land transport. Additional electric load could pose a significant challenge to a grid with high levels of variable and non-dispatchable renewable energy sources. This scenario is not well-examined, nor is the use of pumped hydro energy storage for low-cost energy balancing. In this paper, we investigate the electrification of land transport within a photovoltaics and wind dominated 100% renewable electricity system. Only technologies that are deployed at scale and widely available globally are considered, namely photovoltaics, wind, battery electric vehicles, high voltage transmission, and pumped hydro. As a case study we present an hourly energy balance analysis of the Australian National Electricity Market with 100% renewables and 100% uptake of electric vehicles for land transport. The cost of the system is determined by occasional periods (days-weeks) of low renewable generation, and therefore only weakly dependent on the charging regime. The 40% increase in electricity demand due to electric land transport can be incorporated with a 4%–8% increase in the levelized cost of electricity. An exception occurs if most passenger vehicle charging occurs during the evening peak period, in which case the average price increases by about 18%.

Hundreds of microgrid have been installed and working in the world. In India, the number of install and operating microgrids are more than sixty-seven as per Ministry of New and Renewable Energy. Many Indian rural villages have no electricity, yet they have access to the main grid. MNRE proposed to install 1000 microgrids and minigrids of 500 kW capacity upto 2021. However, No Indian standard and codes published on the microgrid yet. The author compared and presented the different architectures, operation strategies and economic issues of the microgrid on the basis of installed microgrid projects in India. It is challenging to evaluate the economics of microgrid. In this paper, the coordination between the primary, secondary and tertiary control techniques are explained for the optimum operation of microgrid. The author also compared the various economic issues and policies on microgrid.

Water and electricity distribution networks are two highly interdependent and critical infrastructures in the world today. This paper investigates the coupling of water and electricity distribution networks through the electrical energy demand of water pumps. Further, with additional variable renewable energy (VRE) sources in the distribution system, utilities face significant challenges stemming from the VRE stochasticity. In this paper, a coordinated operational model of urban water and electricity distribution networks is proposed. Stochastic VRE generation is handled by using a data-driven probability efficient point (PEP) method based on historical wind and solar datasets without the need for any probability distribution function. Case studies are performed on a modified Institute of Electrical and Electronics Engineers (IEEE) 13-node electricity distribution network coupled with a 10-node water distribution network, typical of a small-town setting. Results show the impact of coordinating water and energy networks on the cost of operation and the distribution locational marginal prices (DLMPs). The inclusion of water tanks as alternative storage devices in the electricity distribution network are shown to slightly reduce voltage violations, line congestion, and VRE curtailments in a case with high VRE penetration.

Considering the impact of the restriction of the weight of the responsibility of consumption on the promotion of local renewable energy consumption and the cross-provincial transaction of renewable energy, this study examines the multi market clearing decision-making problem. Additionally, it proposes and constructs the intraprovincial and interprovincial bi-level market clearing decision-making model. The objective function of the upper model is to minimize the operation cost of the power market at the intraprovincial level considering the weight constraint of consumption responsibility, and the objective function of the lower model is to minimize the renewable energy purchase cost at the interprovincial level. In the two-level model, the two levels are coupled with each other, and the Karush–Kuhn–Tucker (KKT) condition of the lower level model is used to transform the two-level model into a single-level nonlinear model. The strong duality theory and Big M method are used to transform the nonlinear model into a linear model, and the Gurobi solver is used to solve the problem. The results show that the optimal interprovincial renewable energy purchasing power is achieved at the minimum market operational cost in the province. In addition, the effectiveness and rationality of the model are verified by the example analysis.

China has embarked on a new round of power market reforms since 2015. Traditional power generation groups (TPGGs), as important practitioners of the electricity market, have taken various actions to adapt to the new market environment during recent years. Trading performance, the most manifestation of enterprise operation is evaluated in this paper, to help TPGGs explore transition strategies to improve business and achieve a sustainable development. Considering multiple factors influencing TPGG trading performance, a hybrid framework based on Multiple-Criteria Decision-Making (MCDM) under fuzzy rules is proposed. Meanwhile, to deal with the ambiguous language in the evaluation process, the fuzzy Delphi method is combined with triangular fuzzy numbers (TFNs). Thus, twelve key indicators are selected, and the integrated evaluation index system is established. Then, a novel method combining Analytic Network Process (ANP) and Matter-Element Extension Cloud model (MEECM) is applied to evaluate the performance, which cover both fuzziness and randomness of the problem. In addition, an optimized cloud entropy algorithm considering the “3En” and “50% relevance” is implemented in MEECM. At further, through empirical analysis of five power generation companies in China, the proposed evaluation framework is verified. The results show that company A is the best, and more attention should be paid to the criteria related to market and support technology research (Z1), as well as trading capability and behavior (Z2). Finally, a series of sensitivity analyses have been conducted to examine the validity and effectiveness of the established evaluation framework and results. Meanwhile, some suggestions are put forward to improve trading performance for TPGGs. The study improves traditional cloud model and MCDM techniques. Therefore, it can help TPGGs track their course of actions during market reforms, and then take steps to enhance their management capabilities. On the other hand, it provides an effective means for market management agencies to evaluate market conditions through physical performance, to achieve a sustainable development of the power industry.

This paper discusses the impact of cap-and-trade mechanisms, including grandfathering and benchmarking mechanisms, on renewable energy investments and marketing efforts in the electricity market. The current system constructs a two-level electricity supply chain, with a leader's electricity generator and a follower's electricity retailer. Based on the analysis framework of the game theory, the optimal solutions under the grandfathering mechanism and benchmarking mechanism are compared. The results are as follows. First, grandfathering and benchmarking are both conducive to the investment of renewable energy. Further, the benchmarking mechanism is more conducive to investments. Second, benchmarking is beneficial to marketing efforts, while grandfathering is not. Third, the grandfathering mechanism and benchmarking mechanism are conducive to the improvement of market demands. Further, grandfathering produces more total carbon emissions, while benchmarking reduces the total carbon emissions.

The goal of China’s new round of electricity market reform is to orderly release the plan of power generation and power utilization and gradually expand the scale of market-oriented transactions. The generation adequacy is an indicator that needs to be paid attention to during releasing the plan of power generation and power utilization. Insufficient generation adequacy will result in market monopoly and high market-clearing prices. Given this background, the market power and bidding strategies of generators under the low and high generation adequacy are analyzed in this paper. Then, a synthetic decision model for the energy allocation between bilateral contracts and a pool market and the bidding strategy in the pool market is constructed considering the generation adequacy. Case studies are performed for demonstrating the decision-making model and simulation results show that the proposed model can assist generators to make energy allocation and bidding decision based on the generation adequacy, own power generation capacity and bilateral contract prices, and can provide guidance for market operation agencies to gradually release the plan of power generation and power utilization.

Finding an optimal bidding strategy for a wind farm in the electricity market is not straightforward due to the wind variability. This issue is becoming more relevant as renewable plants are more exposed to market signals. Considering the characteristics of the European markets, operators must submit the energy bidding between 12 and 36 h ahead the actual delivery time. This bidding can be adjusted later in any session of the intraday markets, but sometimes this is not enough to reduce significantly the deviation risk. This paper presents a practical application of a method to analytically calculate the optimal bidding of a wind power plant, based on the maximisation of the income function. The results show that, given the characteristics of the deviation markets in Spain, the optimal bidding strategy depends essentially on the deviation of the system. The proposed technique has been tested and validated in a real application by considering actual data for energy production and forecasts for an operating wind farm in Spain, as well as real market deviations and prices provided by the Spanish system and market operators; analysing the advantages of the proposed optimal bidding strategy over the most plausible option, based on bidding the forecasted energy.

Given the background of electricity market, the electricity consumption optimization can reduce consumers’ energy cost, and also help to reduce the peak load and improve the accommodation level of clean energy. Aiming at solving the problem of consumers’ insufficient management of their own electricity consumption, a multi-objective optimization model of electricity consumption is proposed considering the correlation of equipment electricity consumption. First, the cross-correlation coefficient is adopted to identify the consumption correlation among various electricity equipment based on the historical load data of consumers, and the significance of the consumption correlation is analyzed by silhouette index. Next, a multi-objective optimization model is constructed and the optimal electricity consumption strategy is obtained to maximize the consumer-side economy and comfort benefits. Finally, the results of case studies on a Zhejiang stir-fried tea enterprise in China show that the proposed model is feasible and effective.

We analyze the procurement problem in the electricity supply chain, focusing on the interaction between futures and spot prices. The supply chain network analyzed in our study includes risk-averse generators and retailers, both with the ability to use conditional value at risk (CV@R) in their decision processes. In this supply chain, the futures price is computed to clear the futures market, without imposing the constraint that the expected spot price equals the futures price. As major methodological contributions: we compute the Nash equilibrium of the problem using CV@R and considering conjectural variations; we derive analytical relationships between the futures and the spot market outcomes and study the implications of demand and marginal cost uncertainty, as well as the level of the players’ risk aversion, on market equilibrium; we introduce the concept of risk-adjusted expectation to derive the futures market price as a function of the players’ expected losses or profits in the spot market; and we use consistent spot and wholesale price derivatives to calculate the players’ reaction functions. Finally, we illustrate our model with several numerical examples in the context of the Spanish electricity market, studying how the shape of the forward curve and the relationship between spot and futures prices depend on seasonality, risk aversion, generators’ market power, and hydrological resources. Surprisingly we observed that risk aversion increases the profit and reduces firms’ risk, and that the consumer utility is higher in the scenarios in which retailers behave a la Cournot in the wholesale market.

Driven by environmental policy making, electro-mobility has become a crucial tool to mitigate the transport induced CO2 emissions and is being subsidized by Governments, creating more incentives for end users to adopt it. This article addresses the extensive e-mobility transition’s influence on the wholesale electricity market in Lithuania. The analysis is set up to picture the market in 2025, when the number of vehicles is expected to reach around 20 thousand. Three different days are selected, where increased demand exerts significant pressure on the system, and which represent separate characteristic seasons consumption-wise; the corresponding electricity price changes are estimated. With the predicted market behaviour having an additional load from EVs charging, the wholesale price effect is identified and quantified. Prices may rise from about 7.5% in Summer time to almost 20% in the Winter season. These findings raise interesting policy implications and anticipate a discussion regarding societal fairness.

Energy transition is prevalent nowadays, and Colombia is no exception to this phenomenon. Energy coming from non-conventional renewable resources is one of the main feature of this phenomenon. Consequently, this paper evaluates the impact of this new kind of renewable energy resource in the Colombian wholesale electricity market. This evaluation uses a counterfactual scenario based on a structural model of an energy firm’s behavior that offers 1000 MW in 2018. Our results suggest that the spot price decreases on average 12.75 COP$/kWh (4.31 US$/MWh) per day. We find that 6.75 COP$/kWh (2.28 US$/MWh) is due to the firms’ strategies, and 6 COP$/kWh (2.03 US$/MWh) is a merit order effect.

H2 production from solar electricity in the region of the Atacama Desert – Chile – has been identified as strategical for global hydrogen exportation. In this study the full supply chain of solar hydrogen has been investigated for 2018 and projected to scenarios for 2025-2030. Multi-year hourly electrical profiles data have been used from real operating PV plants and simulated Concentrated Solar Power “CSP” plants with Thermal Energy Storage “TES” as well as commercial electricity Power Purchase Agreement “PPA” prices reported in the Chilean electricity market were considered. The Levelized Cost of Hydrogen “LCOH” of each production pathway is calculated by a case-sensitive techno-economic MATLAB/Simulink model for utility scale (multi-MW) alkaline and PEM electrolyser technologies. Successively, different distribution, storage and transportation configurations are evaluated based on the 2025 Japanese case study according to the declared H2 demand. Transport in the form of liquefied hydrogen (LH2) and via ammonia (NH3) carrier is compared from the port of Antofagasta, CL to the port of Osaka, JP. QC 20220808

The growing deployment of utility-scale photovoltaic (PV) in electricity industries raises many concerns for power system operation. Due to the highly variable and somewhat unpredictable characteristics of short-term PV output, the system operator faces challenges in balancing electricity supply and demand, which increases the need for short-term frequency management. Although many studies have been carried out to enhance the understanding of short-term PV output characteristics, the uncertainty around forecast targets for electricity market dispatch has not been studied. This paper analyses 4-s SCADA output data from PV plants and all other generators operating in the Australian National Electricity Market (NEM) to assess the deviation from their forecast dispatch targets. During the study period, PV penetration was less than 5% of the total generation capacity. The deviations of PV plants from forecast targets increases almost proportionally with their operating level. The correlation between deviations from the targets and system frequency deviations shows that PV plants contribute towards increasing frequency deviations, yet the impact seems minor, with only 0.06–0.09 correlation coefficients. Despite their variability during normal operation, PV plants demonstrate accurate output control when following dispatch instructions during network constrained periods with fluctuations of only ±1% of the plant rated capacity.

This paper suggests a novel framework to co-optimize the power and heat in smart cities considering the local air pollution from energy generation units and traffic fumes. To achieve the aim, a smart city comprised of power and heat generation units is addressed. The energy system of the smart city can be connected to the main grid or be operated as a stand-alone power system. Connecting to the main grid, the city trades power in the wholesale electricity market with price uncertainty. In order to co-optimize the operation cost of energy units and to control the pollution levels of the urban area, a Combined Economic Pollution Dispatch Problem (CEPDP) is developed. The CEPDP is optimized under air pollution constraints from both energy generation units and traffic fumes. Increasing the applicability of the suggested approach, pollution density is addressed using the Box model while the wind speed and surface roughness influence on it. To solve the CEPDP, a multi-objective Non-Dominated Sorting Genetic Algorithm (NSGA II) is proposed. The suggested approach is implemented in a virtually-designed smart city located in the center of Iran. The results show that although the use of distributed generation decreases the global emission all over the world, it transmits the air pollution from suburban areas to cities increasing the local pollution. The two conflict objectives, i.e. global and local pollutions, may change the operation of distributed generation to meet the local environment requirements of cities.

The integration of residential photovoltaic (PV) systems for self-consumption is expected to unlock a variety of economic, technical and social benefits, such as the creation of local work and the reduction of the price of electricity, CO2 emissions and electrical power losses in the transmission network. In spite of all these advantages, regulatory barriers prevented the development of renewable self-consumption in Spain prior to October 2018, when a new regulation introduced the right to self-consume electrical energy without charges. This work aims to estimate the effect of residential self-consumption on the Iberian electricity market. To achieve this goal, a ceteris paribus approach was adopted, the accumulated scattered PV self-production was turned into a reduction of the aggregate demand in the market and the new clearing point was established. Based on 2017 market data, the results showed that a 1% curtailment of the annual demand due to PV self-consumption could reduce the cost of the market-traded energy by almost 2%.

The limitations of production subsidies for variable renewable energy (VRE) plant as a means of decarbonising electricity markets are well known. Despite this, production subsidies for VRE plant are the most prevalent form of policy intervention in Australia. In the absence of cost-effective storage, production subsidies for VRE plant can create incentives to produce electricity at times the system may not need it, thereby severing the link between the spot market and financial incentives to generate. Such production subsidies can also reduce the incentive for new VRE plant to participate in ‘firm’ forward derivative markets. In this paper, we examine two adjustments that could be made to production subsidies for VRE plant that would correct for these unintended consequences. Firstly, the quantum of the subsidy could be a function of the wholesale electricity price, electricity demand or emissions; and secondly, new generators would be ineligible to receive production subsidies without demonstrating that they are facilitating the supply of financial derivative contracts. The effect of both adjustments would be to increase the incentive of VRE plant to demand-follow; that is, make its output ‘firmer’.

The contribution of solar energy to electricity production has intensified in recent years. In this study, we aim to measure the impact of higher solar penetration on electricity prices across regional and national electricity markets in Australia. Using daily data, we also estimate volatility transmission among the Australian electricity markets. Univariate and multivariate GARCH models are employed to achieve the objectives of the study. The results show that increasing solar penetration decreases electricity prices in the regional electricity markets of Queensland, South Australia and Tasmania. However, the results further reveal that the joint negative effect of solar energy on electricity prices is outweighed by the positive impact of natural gas prices. Finally, the findings show that there are many significant own-volatility and cross-volatility spillovers among the Australian electricity markets. Among the regional electricity markets, the New South Wales electricity market emerged as the most critical market in terms of cross volatility.

In this work, a model-based analytical framework is proposed to evaluate the cost-effectiveness of process design alternatives with respect to their ability to participate (particibility) in Demand Response (DR) services (such as load shifting under the Day-Ahead market) through the use of supply curves. The supply curves relate available DR capability to the investment cost of process design, and are constructed based on a specific DR service. Using the California Independent System Operator (CAISO) wholesale electricity market as a reference, the proposed framework is implemented to demonstrate the cost-benefits of considering DR aspects at the design stage. As a motivating example, the developed framework is illustrated using a CSTR-storage model, for which supply curves are generated under different scenarios. A visualization is provided of the effect of the process design capacities on the DR capability, as well as the limitations on the load-shifting capacity of a given process design.

The current state of technology development enables the provision, of various system services by distribution system operators via energy storage. The quality of services offered by the energy storage is unavailable for the classical methods used so far. According to the adopted policy of the European Union (EU) soon can be expected to, among others, dynamic development of the e-mobility sector and renewable energy sources, which will be directly related to the appearance of new types of loads on the electricity market. Revolutionary changes related to the development of e-mobility technologies require the development of new system services offered by Distribution System Operators (DSOs). The article aims to present the growing model of evaluation of energy storage facilities constructed based on various technologies in the operation of DSOs.

This article examines the additional profit that can be achieved with the integrated operation of an on-site electrolyser, a hydrogen tank, a photovoltaic system, and a wind power plant based on Hungarian data from 2019. The results of the optimisation show that the system economically reduces the volatility of weather-dependent renewable production, so there is a promising demand-side management potential in coordination. We found that the operating profit is highest in April at EUR 19,416, 18,932 in July, and lowest at EUR 17,075 in January. The production curve of photovoltaic capacities is better matched to fuel demand, so increasing the share of solar energy results in lower balancing activity but higher profits. Increasing the size of the hydrogen storage and electrolyser, with constant hydrogen demand and prices, will cause a convergent increase in profits, however above a 10 kg storage capacity or 350 kW electrolyser capacity there is no substantial profit increase. In the case of the economically optimal asset size, there is a slight competition between the electricity market and the hydrogen distribution activity. The choice between the two activities depends on current electricity and hydrogen prices and the cost of unmet hydrogen demand.

This paper presents an optimisation model to assess the techno-economic feasibility of a hydrogen refuelling station, which purchases power from the electricity market, supplies the mobility sector with hydrogen, and participates in the ancillary service market. The problem is formed as a mixed-integer nonlinear programming model to investigate the optimal operational plans considering the nonlinear behaviour of an electrolyser and grid costs calculation model. Obtained results from various scenarios in 2020 and 2030 show that participation in the reserve market considering optimal sizing and dispatch of components increase revenues up to 16%, and as a result, decrease the hydrogen break-even price by up to 4.7% and 6.4% in 2020 and 2030, respectively. Exemption from tax and levies for connection to the grid reduces the hydrogen break-even price by up to 13%. Plant operators could benefit from the proposed approach to schedule components reliably while meeting the hydrogen demand and maximising the annual profits.

The profitability of investments in photovoltaics (PVs) and batteries in private households depends on the market price of electricity, which in turn is affected by the investments made in and the usage of PVs and batteries. This creates a feedback mechanism between the centralised electricity generation system, and household investments in PVs and batteries. To investigate this feedback effect, we connect a local optimisation model for household investments with a European power generation dispatch model. The local optimisation is based on the consumption profiles measured for 2104 Swedish households. The modelling compares three different scenarios for the centralised electricity supply system in Year 2032, as well as several sensitivity cases. Our results show total investment levels of 5–20 GWp of PV and 0.01–10 GWh of battery storage capacity in Swedish households in the investigated cases. These levels are up to 33% lower than before market feedback is taken into account. The profitability of PV investments is affected most by the price of electricity and the assumptions made regarding grid tariffs and taxes. The value of investments in batteries depends on both the benefits of increased self-consumption of PV electricity and market arbitrage.

The transition from fossil fuels to renewable energy sources is critical to reduce future emissions and mitigate the consequences hereof. Yet, the expansion of renewable energy, especially the highly fluctuating production of wind energy, poses economic challenges to the existing energy system in Denmark. This paper investigates the economic feasibility of integrating a 250 kW, 500 kW, 750 kW and 1 MW water electrolysis system in the existing Danish energy market to exploit excessive off- and onshore wind energy for hydrogen production used as fuel for transportation purposes. In 2018, Danish wind turbines produced excess energy during 1238 h, which poses a capacity constraint as the electrolysis systems are limited to only produce hydrogen for 14% of the total available annual hours. This paper concludes that the net present value of each investment is negative as the fixed and variable production costs exceeds the generated revenues and it is therefore not economical feasible to invest in an electrolysis system with the purpose of only operating whenever excess off- and onshore wind energy is available.

China’s electricity market reform poses an urgent demand for grid parity of renewable energy (RE), however, the high initial investment cost makes it difficult to achieve. Although feed-in tariff (FIT) and renewable portfolio standards (RPS) schemes are both practical policy tools to support the development of the RE industry, which policy is more consistent with the goal of grid parity? Accordingly, based on the system dynamic model, this research studies the levelized cost of RE (LCOE) under FIT and RPS, respectively. The results show that: First, under the current FIT scheme, wind (onshore) and photovoltaic (PV) power both cannot achieve grid parity. To realize it, extra payouts in four categories of areas should at least be 0.236, 0.244, 0.299 and 0.261 CNY/kWh. Similarly, additional subsidies for PV in Tibet and the northwest region except Tibet need to be 0.924 and 0.764 CNY/kWh. Second, under the RPS, wind (onshore) power can basically achieve grid parity, but PV cannot. Subsidies for PV need at least to be 0.525 and 0.364 CNY/kWh to achieve grid parity. Therefore, compared with the current FIT in China, the policy combination of FIT and RPS adjusted to local conditions is more conducive to realizing grid parity.

The financial risks imposed from the uncertain parameters is a considerable issue in the optimization problem of renewable-based energy systems. Due to the various risks in renewable-based energy systems, a practical and simple risk measurement approach can be efficient in the risk-based strategy selection process. In this paper, the downside risk constraints (DRC) approach as a novel risk measurement approach is proposed to manage the imposed risks from the uncertain parameters over the stochastic problems. Therefore, various uncertainties, including solar irradiation, temperature, wind speed, electricity demand, and electricity market price uncertainties, are modeled using the DRC approach along with the stochastic programming. In addition, the compressed air energy storage (CAES) and demand response program (DRP) is implemented to manage the imposed risks. By using the proposed risk measurement method, the system operator can obtain a risk-strategy that is independent of scenarios. According to the obtained results, the expected cost of the stochastic problem is $ 6145.62, which by using the DRC approach, the system operator by paying the 3.4% ($ 6353.5) more cost can guarantee itself against the financial risks. The advantage of the proposed DRC approach is the conversion of the stochastic problem to a deterministic scenario-independent problem.

Electricity use varies with the weather as changes in temperature and humidity affect the demand for space heating and cooling. The residential end-use sector has the largest seasonal variance with significant spikes in demand every summer and winter. Electricity demand is subject to fluctuations on a seasonal basis, across the week, and during the day and can also be influenced by irregular events. The demand for power fluctuates significantly in the electricity market resulting in significant ancillary costs to suppliers. This article describes weather derivatives in electricity markets and applies the risk management hedging technique for the price fluctuation and electricity demand during weather variations. The main objective of this paper is to construct a temperature stochastic model for option pricing and determine its impact on electricity markets. We begin by briefly considering the construction of the temperature stochastic model under a Fractional Brownian motion which is driven by the fractional Ito formula and the fractional Girsanov theorem. We then extend this staging to the weather derivative market and construct a stochastic model for bond weather derivatives and financial derivatives (weather options). Following that, we construct and derive the option-pricing model from the Black–Scholes equation and build a pricing model for weather derivative instruments based on the weather contribution of the electricity market. Finally, we carried out a numerical example that allows us to see that the predicted option pricing values might differ depending on how temperature is forecasted.

Over the last decades, the actions aimed to realize an integrated pan-European electricity market brought about profound modifications on the physical, information and communication systems upon which the electricity delivery process occurs. In order to assess some of the techno-economic challenges relating to the single electricity market functioning, including the future needs of electricity, several dispatch models have been developed in recent years in Europe and the rest of the world. In particular, EUPowerDispatch, a minimum cost dispatch model, was originally developed to investigate the need for cross-border transmission investment in Europe. This paper proposes an improved version of EUPowerDispatch that aims at analysing the generation adequacy of the European electricity system in a probabilistic manner. For this purpose, planned and unplanned outages of all single conventional generating units are modelled. The former are optimally and timely allocated while the latter are stochastically generated with chronological Monte Carlo simulations. Data of wind and solar PV production from several meteorological years are accounted for as well as hydro series of run-of-river and natural inflows to the hydro reservoirs. The case study is composed of 33 European countries/regions which are linked through interconnectors. Indexes for the reliability assessment of electric power systems are calculated in order to study the generation adequacy of the European electricity system in 2025.

Long-term economic viability of offshore wind power is not only reliant on reducing installation, commissioning, and operations and maintenance costs, but also on the elimination of subsidies and grants. Current economic analyses use historical price and cost data to predict the levelised cost, net present value, payback period and internal rate of return from offshore wind. These analyses use parameters such as water depth at site, number and size of turbines, grid connection costs, equipment costs, revenue from the wholesale market price of electricity, operations and maintenance costs, revenue from subsidies and the cost of finance amongst others to build a model to determine the viability of the array. Here we review the challenges of accurately estimating levelised cost of energy (LCOE) for offshore wind outlining differing approaches to calculating LCOE, the factors influencing this, and the impact of variation in LCOE calculation. Current costs for the production of offshore wind energy are summarised based on publicly available datasets.

To implement the process of European power markets’ integration, a market-clearing algorithm has been developed among European power exchanges under the title EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), being a strictly economic dispatch algorithm and providing several options and products to market participants. This paper proposes an optimization-based methodological framework for the optimal economic dispatch problem in power exchanges, further enhancing the EUPHEMIA algorithm’s block order module. More specifically, through the development of a mixed-integer linear programming (MILP) model and utilizing an iterative process, it quantifies the impacts of the proposed new options to the optimal energy mix, the wholesale prices, and on the market players’ economic performance. The model considers all the current market products of the EUPHEMIA algorithm, as well as introduces new market products such as the flexibility provision of the main aspects of block orders (minimum acceptance ratio, price offer, and block time limits), the modification of existing block orders (exclusive groups), the development of new linkage structure (linked block orders with parallel relationship), as well as the activation of mixed schemes combining existing types of block orders into new integrated forms. The developed optimization framework has been assessed in the Greek interconnected power system, including its interconnections with neighboring power systems. The proposed methodological approach suggests a robust and systematic methodological formation to provide useful insights on policy issues and ongoing debate regarding the shaping of more efficient market designs and structures to deal with the new operational challenges of low-carbon flexible power systems.

Increasing amounts of fluctuating renewable energy lead to decreasing electricity prices and impair security of electricity supply. Consequently, sustainable and economically feasible solutions need to be found to ensure both ongoing renewable energy expansion and stable electricity supply. We examine the impact of batteries on security of the electricity supply and achieving renewable energy expansion. For this purpose we develop an electricity market model that enables the simulation of batteries both as an economic-driven investment option and as a government subsidized option. We present six policy scenarios in which batteries are utilized as an option that is subsidized by the government to secure electricity supply and engender renewable energy expansion. Our simulations, based on empirical data, indicate that, in a free market, battery investments are not profitable for private investors. On the other hand, these six policy scenarios show that by subsidizing investments in batteries governments could ensure a secure electricity supply as well as ongoing renewable energy expansion. A comparison to similar policy scenarios that do not adopt batteries indicates that the total sum of government subsidies and external costs is up to 36% lower when utilizing batteries.

Wind power, the most promising renewable energy source in the world, plays an important role in the electricity markets. Wind power curtailment cannot be avoided in some countries due to its output has a special feature of randomness and volatility. Since the excess wind power being converted into hydrogen and sold to the hydrogen market will be the future trend. This study proposes a wind-electrolytic hydrogen storage system to participate in the electricity/hydrogen markets for selling electricity and hydrogen, which can help to improve the benefits of wind power in the electricity markets and addree the wind power curtailment effectively. With considering the uncertainties of wind power outputs and electricity prices, the optimal operation strategy is proposed with the objective of maximizing profits. The scenario-based stochastic method is adopted to describe the uncertainties, and the financial risk is evaluated using conditional value-at-risk. The operational problem of the proposed system is formulated into a mixed-integer linear programming model. Finally, the feasibility of the proposed operational strategy is validated by a case study. The results show that the expected revenue increases with the increase of the hydrogen selling price, indicating that investors can obtain profits by converting electricity into hydrogen. The optimal expected revenue increases by 33.42% when hydrogen price increases from 1.2 DKK/kWh to 1.8 DKK/kWh and the risk factor is equal to 0. Based on the analysis of the results, the importance of hydrogen can be proven. Wind power, the most promising renewable energy source in the world, plays an important role in the electricity markets. Wind power curtailment cannot be avoided in some countries due to its output has a special feature of randomness and volatility. Since the excess wind power being converted into hydrogen and sold to the hydrogen market will be the future trend. This study proposes a wind-electrolytic hydrogen storage system to participate in the electricity/hydrogen markets for selling electricity and hydrogen, which can help to improve the benefits of wind power in the electricity markets and addree the wind power curtailment effectively. With considering the uncertainties of wind power outputs and electricity prices, the optimal operation strategy is proposed with the objective of maximizing profits. The scenario-based stochastic method is adopted to describe the uncertainties, and the financial risk is evaluated using conditional value-at-risk. The operational problem of the proposed system is formulated into a mixed-integer linear programming model. Finally, the feasibility of the proposed operational strategy is validated by a case study. The results show that the expected revenue increases with the increase of the hydrogen selling price, indicating that investors can obtain profits by converting electricity into hydrogen. The optimal expected revenue increases by 33.42% when hydrogen price increases from 1.2 DKK/kWh to 1.8 DKK/kWh and the risk factor is equal to 0. Based on the analysis of the results, the importance of hydrogen can be proven.

The operation of power-to-X systems requires measures to control the cost and sustainability of electricity purchased from spot markets. This study investigated different bidding strategies for the day-ahead market with a special focus on Sweden. A price independent order (PIO) strategy was developed assisted by forecasting electricity prices with an artificial neural network. For comparison, a price dependent order (PDO) with fixed bid price was used. The bidding strategies were used to simulate H2 production with both alkaline and proton exchange membrane electrolysers in different years and technological scenarios. Results showed that using PIO to control H2 production helped to avoid the purchase of expensive and carbon intense electricity during peak loads, but it also reduced the total number of operating hours compared to PDO. For this reason, under optimal conditions for both bidding strategies, PDO resulted in an average of 10.9% lower levelised cost of H2, and more attractive cash flows and net present values than PIO. Nevertheless, PIO showed to be a useful strategy to control costs in years with unexpected hourly price behaviour such as 2018. Furthermore, PIO could be successfully demonstrated in a practical case study to fulfil the on-demand requirement of an industrial captive customer.

One of the key challenges that still facing the adoption of renewable energy systems is having a powerful energy storage system (ESS) that can store energy at peak production periods and return it back when the demand exceeds the supply. In this paper, we discuss the costs associated with storing excess energy from power grids in the form of hydrogen using proton exchange membrane (PEM) reversible fuel cells (RFC). The PEM-RFC system is designed to have dual functions: (1) to use electricity from the wholesale electricity market when the wholesale price reaches low competitive values, use it to produce hydrogen and then convert it back to electricity when the prices are competitive, and (2) to produce hydrogen at low costs to be used in other applications such as a fuel for fuel cell electric vehicles. The main goal of the model is to minimize the levelized cost of energy storage (LCOS), thus the LCOS is used as the key measure for evaluating this economic point. LCOS in many regions in United States can reach competitive costs, for example lowest LCOS can reach 16.4¢/kWh in Illinois (MISO trading hub) when the threshold wholesale electricity price is set at $25/MWh, and 19.9¢/kWh in Texas (ERCOT trading hub) at threshold price of $20/MWh. Similarly, the levelized cost of hydrogen production shows that hydrogen can be produced at very competitive costs, for example the levelized cost of hydrogen production can reach $2.54/kg-H2 when using electricity from MISO hub. This value is close to the target set by the U.S. Department of Energy.

This study proposes a bidimensional empirical mode decomposition based conditional value at risk approach to explore the risk spillover effects between carbon market and electricity market. Firstly, bidimensional empirical mode decomposition is applied to decompose the pair of carbon and electricity prices into a series of simple modes with different frequencies. Secondly, conditional value at risk is used to capture the risk spillover effects between carbon market and electricity market at each frequency. The empirical results show that the risks of high frequency modes are higher than those of intermediate and low frequency modes. Carbon market has a positive risk spillover on electricity market, and electricity market has a negative risk spillover on carbon market. For high and low frequency modes, the risk spillover effects from carbon market to electricity market are positive, however those from electricity market to carbon market are negative. For intermediate frequency modes, there are bidirectional negative risk spillover effects between carbon market and electricity market.

Residential consumers have electrical and thermal loads. Therefore they can be utilized hybrid thermal and electrical energy systems to procure their required energy. In the proposed system, in order to supply residential loads, a hybrid energy system (HES) is proposed which consists of photovoltaic/solid oxide fuel cell/thermal and electrical storages/boiler. Also, the uncertain parameters such as thermal and electrical loads, electricity market price, and solar irradiation are considered in the stochastic formulation. Uncertain parameters can be led to financial risks in the system operation. In order to measure imposed risks, in this paper, a novel risk management method called downside risk constraints method is used to model the financial risks imposed from the uncertain parameters. According to obtained results, the operator of the hybrid energy system by utilization of the downside risk constraints method has obtained a strategy that is scenario independent. In other words, the downside risk constraints method by minimizing the imposed risks introduced a zero-risk strategy which operation cost would not increase by changing the scenario. Results are shown that system operators by paying 1.3% more expected cost ($ 40.22 instead of $ 39.69), can make its operation independent of the scenario. Also, risk-based operational strategies of the proposed hybrid energy system are reported in the results as graphical results. The proposed risk-measurement operation problem of the designed hybrid energy system is formulated as a mixed-integer linear programming (MILP) model and modeled by GAMS software using CPLEX solver.

Hydropower energy is one of the most economical and cleanest energies in the world. Due to the huge investment in hydropower projects, economic feasibility and determining the main design parameters related to plant sizing of hydro projects are extremely important. In this research, a simulation–optimization model for optimal design of hydropower systems with a systematic view of the basin (which is a complex nonlinear problem) has been developed. For water resources planning and management in the water basin, WEAP model has been used. Despite various capabilities, there are also limitations for hydropower systems modeling in WEAP. In this regard, a hydropower computation module has been employed to resolve these disadvantages. Then, PSO algorithm, linked to the simulation model and the developed optimization-simulation model has been used to solve the problem of optimal design of Garsha, Kuran Buzan, Sazbon and Tange mashoore power plant projects in Karkhe river basin. Objective function has been formulated based on two conventional methods in the economic analysis of hydro projects, i.e. electricity market price method and alternative thermal plant method. The objective function is maximization of net benefits of hydropower projects considering all resources, uses, and demands in the basin. Normal water level (NWL), Minimum Operation Level (MOL), Installed Capacity (IC) of dams and power-plants were chosen as decision variables of the problem. Finally, the results of optimal design of hydropower system have been discussed for both economic objective functions, followed by comparison and analysis of the results. the total generated energy and firm energy of the hydropower system is calculated bigger in alternative thermal plant method compared to market price method. However, the peak generated energy is computed larger in the market price.

The contract for difference (CfDs) auctions are the cornerstone of the UK electricity sector's decarbonization policy and were introduced as part of the Electricity Market Reform in 2013. The CfD auctions appear to have been successful in achieving low bids for low-carbon technologies, especially offshore wind power. However, the design of the auction increases the probability of speculative bidding, while the one-shot nature of the auction prevents bidders from learning and from utilizing information efficiently. We show that implementing a stringent non-delivery penalty to induce truth telling can improve deployment rate without increasing support costs. Moreover, by holding regularly scheduled (annual, for example) auctions, information on technology cost decreases can be better incorporated into the bids, lowering investor uncertainty and thus having a positive effect on support costs.

We study generation-capacity planning in a restructured oligopolistic electricity market, taking a conjectural-variations approach. We do this through a two-stage model that captures an initial set of investments followed by equilibria in a series of spot electricity markets, in which firms make production decisions. Although we model the generating firms as being quantity-setters, we do not model a Nash–Cournot equilibrium. Instead, we assume that the firms endogenize the impacts of their production decisions on rivals through reaction parameters, giving a conjectural-variations model of the spot-market equilibrium. Equilibrium conditions in each spot market, as a function of the investment decisions, are derived. This allows characterizing an equilibrium at the investment stage. The proposed model allows the derivation of analytical expressions that characterize such multi-stage equilibria. This proposed model provides insights on the outcomes and characteristics of investment decisions in an imperfectly competitive market setting. Such insights may allow policymakers to understand the efficiency implications of oligopolistic market structures.

This paper evaluates the economic (electric power rates and utilities revenues), environmental (GHG emissions), social (job creation) and political (tax collection) impacts of a wider diffusion of photovoltaic distributed generation (PVDG) and energy efficiency (EE) measures on the markets of electric power distribution utilities in Brazil. The impacts are calculated for three substantially different power distribution utilities, which are representative of the types of utilities found in the country. Key indicators were selected to reflect the main interests of the following stakeholders: power distribution utilities, the government (Federal, State and Municipal) and low-voltage consumers. The results show that the most important issues that should be addressed concerning a large diffusion of EE measures are tax collection and tariff rises. The simulations show that tariff rises can be hampered by EE actions targeted to specific locations and specific actions. The most important impact of PVDG diffusion is on tariff rises, considering the current regulatory framework in the country. On the other hand, the net jobs creations of EE measures and, particularly, PVDG are significant and the cost of job creation in relation to fiscal waiver by investment on PVDG is cost-effective. The GHG emissions avoided by the higher levels of PVDG and EE in 2040, simulated in this study, showed to be significant for both EE measures and PVDG.

We address the problem of trading energy flexibility, derived from pools of residential Photovoltaic and battery-storage systems, to the Day-ahead electricity market. By flexibility, we imply any additional energy that can be stored to or withdrawn from the participating batteries/households at a given time during the next day. The optimization variables include the selection/activation of a subset of participating batteries and the amount of flexibility that should be extracted. Furthermore, the optimization objective corresponds to the expected forecast revenues that can be generated by trading this flexibility to the Day-ahead electricity market. Given the high computationally complexity of a full scale optimization in the case of a large number of participating batteries, we propose a reinforcement-learning-based methodology, which admits linear complexity with the number of participating batteries. The proposed methodology advances prior work with respect to the integration of a large number of batteries. Furthermore, it extends prior work of the authors with respect to providing analytical performance guarantees in comparison with the baseline/nominal operation of the battery. Finally, we compare through simulations the performance of the proposed method with a Linear-Programming-based optimization that provides the exact optimum.

Smart grids (SGs) have been increasingly regarded as an enabling technology for post-Fukushima energy transitions. SGs require new policies and market infrastructures to deliver their potential, but the roles of governments in increasingly market based energy systems have not been well conceptualised. Advancing the socio-technical energy transitions literature, this paper proposes five functions of government-market dynamics in an integrated framework, and applies the framework in the field of smart grid developments in two Asian countries, China and Japan. Based on interviews with 38 key stakeholders, this study has three main findings. First, both countries have in common that the five functions of government-market dynamics are critical in developing, diffusing and utilising SG technologies. Second, China and Japan exhibit distinctive characteristics in the ways that government actors engage market actors. While the Chinese approach is more hierarchical, fragmented and homogenous led by two monopolised grid companies, the Japanese approach is a relatively systemic, bottom-up, and heterogeneous system mainly operated through four large-scale SG demonstration projects. Third, national contextual differences, most notably the advancement of electricity market reforms, explain the variety of the dynamics and outcomes. This paper concludes that consideration of optimising government-market dynamics is vital to create conductive conditions for realising the potential that SGs can offer in energy transitions.

A central challenge facing Mobility as a Service (MaaS) is mispricing of its a core input: the use of scarce road space. A transparent real-time market for road use is essential for MaaS to reach its full potential. We focus on how network-wide, real-time markets for road use support MaaS, and how such markets can be developed. In our proposed network-management scheme, roadway tolls (for entire trips – from origin to destination) are determined in a two-stage market hosted by an independent system operator or “ISO”. Service providers purchase the product (the right to use a series of road segments at a reasonably specific time of day) in the day-ahead market. In real-time, the market becomes physical and operates under the principle of open access: road capacity cannot be withheld in real time and its use is determined by users’ decisions, guided by prices and suggested routings. Real-time road-use prices are computed using clearing prices that balance real-time supply and demand. Those with pre-paid slots can be paid to delay their travel, to create space for high bidders during periods of suddenly low capacity or unexpectedly high demand. Such policies and programs can avoid excessive congestion, provide reliable travel times, and keep traffic moving, especially as automation makes car and truck travel easier. Such policies are critical in helping cities and regions avoid gridlock. They ensure that travelers internalize congestion externalities, while enabling MaaS and other transport providers to deliver higher-quality mobility service for all travelers. Thoughtful marriage of week-ahead, day-ahead and real-time road pricing for travelers on congested networks can deliver efficient transportation systems that save time and energy, while providing signals for optimal infrastructure investment.

In this paper, we take an optimization-driven heuristic approach, motivated by dynamic programming, to solve a class of non-convex multistage stochastic optimization problems. We apply this to the problem of optimizing the timing of energy consumption for a large manufacturer who is a price-making major consumer of electricity. We introduce a mixed-integer program that co-optimizes consumption bids and interruptible load reserve offers, for such a major consumer over a finite time horizon. By utilizing Lagrangian methods, we decompose our model through approximately pricing the constraints that link the stages together. We construct look-up tables in the form of consumption-utility curves, and use these to determine optimal consumption levels. We also present heuristics, in order to tackle the non-convexities within our model, and improve the accuracy of our policies. In the second part of the paper, we present stochastic solution methods for our model in which, we reduce the size of the scenario tree by utilizing a tailor-made scenario clustering method. Furthermore, we report on a case study that implements our models for a major consumer in the (full) New Zealand Electricity Market and present numerical results.

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals.

Infrastructure innovation in the power system industry encourages more partakers to participate in the electricity market which improvises the load utilization level. So, the maintenance of power system’s agility with respect to any dynamic update in terms of load level is necessary. Precise prediction of maximum allowable loading point helps to enhance the power system agility and also improvises the total allowable power transfer capability which in turn helps to supply continuous eminent power supply at the minimal cost to the customers by means of encouraging more contracts. Considering the above potential benefits, in this papear by using individual incremental loading factor (IILF) the precise prediction of total loadability limit (TLL) of the system is manipulated with the help of newly evolved meta-heuristic optimization algorithm such as Grey Wolf (GRW) optimizer and Flower Pollination Algorithm (FPA). The allowable single line contingency scenario is considering along with base case scenario to extract the more realistic TLL which helps to maintain the power system balance with respect to the dynamic nature of the load. The proposed maximum loading point extraction manipulation solution problem is tested with the help of three standard IEEE systems such as 30 Bus, 57 Bus and 118 bus systems. The extracted test results show that the predicted maximum allowable loading point enhances the load utilization level without affecting the system securities. The statistical performance measures of GRW and FPA confirmed the better balance of exploration and exploitation in extracting the optimal results.

Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting of electricity prices has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used to integrate the seasonal behaviour. This paper shows that simple neural network architectures like DNNs with an embedding layer for seasonality information can generate a competitive forecast. The embedding-based processing of calendar information additionally opens up new applications for neural networks in electricity trading, such as the generation of price forward curves. Besides the theoretical foundation, this paper also provides an empirical multi-year study on the German electricity market for both applications and derives economical insights from the embedding layer. The study shows that in short-term price-forecasting the mean absolute error of the proposed neural networks with an embedding layer is better than the LSTM and time-series benchmark models and even slightly better as our best benchmark model with a sophisticated hyperparameter optimization. The results are supported by a statistical analysis using Friedman and Holm's tests.