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Improved end-user engagement is considered to be a key factor in decarbonization efforts towards climate neutral energy systems. While first adopters are already actively seeking ways how to optimize their energy consumption, a true shift in consumer behaviour can only be achieved if the financial benefits are well established and presented. The study aims at estimating the economic performance of end-user engagement in provision of demand response services using air-to-air heat pumps as the underlying technology. The results of the paper help evaluate in a real data setting whether the existing market framework provides sufficient incentives to facilitate end-user participation in the demand response service.

Energy planning and climate policy require understanding long-run energy demand patterns. Electricity demand further is important because energy services derived from electricity typically do not have substitution possibilities from other fuels. By employing dynamic panel models, we estimate the long-run price and output elasticities of aggregate industrial electricity demand for high-income (mostly OECD) and middle-income (mostly non-OECD) countries. The unbalanced data span 1978–2016 and include 35 high-income countries and 30 middle-income countries. Our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. We believe these are the first such panel estimates for middle-income/non-OECD countries and among the few such estimates for high-income/OECD countries to appear in the literature. The output elasticity for high-income countries typically was significantly below unity, around 0.5, and the price elasticity was around − 0.25 (and was statistically significant). For middle-income countries, the output elasticity was greater than unity and was likely significantly larger than the output elasticity for high-income countries, whereas the price elasticity was small and insignificant for middle-income countries.

The article analyzes electricity prices in Slovakia, their distribution tari component, and their significant impact on small-scale photovoltaic power plants (PVPs). Fixed part of the distribution tari component in Slovakia varies considerably, given the region of operation of the distribution system operator as well as the allocated tari rate. Profitability of the small-scale PVP in Slovakia is widely discussed, with differing opinions of the lay and professional public. The article will explain under what circumstances all the opinions may be true. Profitability predictions available online or done by PVP installers are extremely simplified and lead to misleading results. The existence of fixed and variable components of the price plays a significant role and a simple change of the electricity tari may bring significant savings and shorten the payback time of the PVP investment. However, this is a complex issue and requires several other factors to be considered, too. The most important ones are the fixed component of the electricity price, household consumption diagram and the distribution system to which the household is connected.

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011.

Wind generation fluctuation results in the electricity supply uncertainty of the company in electricity market. And this uncertainty has an important impact on hourly electricity price. In our pape...

With the advancement of power market reforms, electricity price prediction has attracted increasing attention. This paper proposes a novel probabilistic forecasting approach based on deep neural network for electricity prices. Firstly, reasonable price distributions are constructed from historical data based on the nearest neighbors. Then, a deep convolutional neural network(DCNN) is employed to extract high-level features. Meanwhile, these features are fed to label distribution learning forests (LDLFs) to generate probabilistic forecasts. The proposed framework, dubbed DCNN–LDLFs, can jointly learn the price distributions. The DCNN–LDLFs provides three types of forecasts, including deterministic forecasts, prediction intervals (PIs), and probability density functions. Unlike most popular models, the DCNN–LDLFs can be trained in an end-to-end manner, which has the opportunity to obtain a globally optimal solution. The case study on Singapore shows that the proposed method provides superior forecasts over the existing approaches.

Purpose Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts.. Design/methodology/approach This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Research limitations/implications The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting. Practical implications When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors. Originality/value The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

The purpose of this paper is to analyze the effects of electricity price changes and energy efficiency subsidy on household energy efficiency purchase and/or behavioural adjustment decisions. The a...

The objective of this study is to propose a dynamic generator maintenance scheduling (GMS) algorithm for long-term power sector forecasting and planning studies in which electricity price and the resulting supply composition are determined with merit-order dispatch. Compatible with the GMS algorithm, a reasonable strategy for the utilisation of storage hydropower plants along with clear definitions for each stage including must-run renewable electricity generation modelling, calculation of reserve capacity, derivation of scenarios for storage hydropower plants and problem formulation is presented. Generation from storage hydropower plants are modelled such as must-run and price-dependent parts, to better approximate reality. The proposed structure is tested with real data of the Turkish system, with a demand and capacity projection in the long term. The results are compared with the actual maintenance plan of the base year and the general profile is evaluated as satisfactory. The results show that the GMS plan and profile may significantly change based on hydro and renewable generation expectation, future capacity evolution, and storage hydropower plant utilisation. Therefore, the proposed GMS algorithm can be utilised especially in long-term price forecasting and supply modelling studies, instead of using a fixed factor to represent the maintenance effect on available generation capacity.

This study proposes a novel scheme for dynamic distribution expansion planning (DEP) in the presence of plug-in electric vehicles (PEVs). The model considers investment, production and maintenance cost and identifies the substations and feeders to be built, reinforced or replaced. Owing to the increasing penetration of PEVs into the distribution network, traditional strategies to expand the network have to be updated to cope with the new uncertainties incurred by the PEVs integration. In this regard, a two-stage scenario-based strategy is presented, in which the uncertainties related to the PEVs are modelled via stochastic optimisation using Monte Carlo simulation. In the first stage, the binary decision variables (here and now decision variables) are determined, whereas in the second stage, the optimal production of substations and optimal charging of PEVs (wait and see decision variables) are identified in a day-ahead electricity market. Moreover, the daily electricity price and load volatilities have also been taken into account. The DEP problem is formulated as a mixed-integer linear programming problem and is solved using the efficient Benders’ decomposition algorithm. The results of the case study based on a 24-node distribution system show the feasibility, tractability and effectiveness of the proposed model.

In this paper, a compartmentalization strategy is proposed for the economic operation of a hybrid AC/DC microgrid. The hybrid AC/DC microgrid will be compartmentalized into several independently controlled and coordinated AC and DC nanogrids. Accordingly, the optimal economic operation of the microgrid consists of two segments, including the optimal power sharing among local DERs in individual nanogrids and the optimal power sharing among interconnected nanogrids. For each AC/DC nanogrid, the optimal power sharing and frequency/voltage restoration are realized simultaneously without the need for any real-time communication. In the compartmentalized microgrid, each nanogrid will communicate with neighboring nanogrids through a sparse communication network to regulate power exchanges among nanogrids. In islanded mode, incremental costs of nanogrids are unified for realizing the optimal economic operation of the microgrid. In grid-connected mode, the external electricity price (i.e., purchase/sale electricity price set by the utility grid) is the desired incremental cost, which would be imposed on some pinned nanogrids and propagated among the remaining nanogrids. Then, the optimal power sharing of nanogrids within the grid-connected microgrid would be modeled as a pinning synchronization problem. The proposed compartmentalization strategy ensures that the optimal economic operation of the hybrid AC/DC microgrid is achieved under both island and grid-connected modes while the utilized communication structure is simplified and the dependency on communication networks is reduced. The performance of the proposed compartmentalization strategy is tested and verified through time-domain simulations in PSCAD/EMTDC for a hybrid AC/DC microgrid located at Illinois Institute of Technology (IIT).

The electricity price plays an important role in stimulating electric vehicles (EVs) to participate in the power grid's scheduling. It is necessary to formulate a reasonable discharging price for the power grid and electric vehicles. Here, a negotiation strategy of electric vehicles participating in optimal scheduling under the multi-agent situation which aims to formulate reasonable discharging price is proposed, and then a two-stage negotiation model considering multiple agents is established. First, a charging and discharging optimisation scheduling model considering EV travel characteristics is proposed, based on which the bidding limits of the power grid and EV agents are calculated. Then, the negotiation process is divided into two stages. In the first stage, all negotiators offer tentative bidding; in the second stage, negotiators will adjust their bidding based on learning other negotiators and the discharging price is obtained finally. In numerical cases, the proposed negotiation model is proved to be effective in balancing benefits of power grid and electric vehicles as well as peak load shifting.

The residential electricity demand were studied using the panel dataset of 21 cities and counties in Taiwan from 1998 to 2018. Observations were grouped and estimated separately for the urban and r...

Over 1 GW of distributed wind has been installed across all 50 states, the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands. Yet the number of installations varies greatly by state, even when wind resources are similar. This article presents the results of a county-by-county study of markets for distributed wind in the contiguous United States that culminates with a figure that represents the relative strength of markets across the United States based on a composite assessment of 1) policy environments, including incentives, and interconnection, net metering, and permitting policies; 2) electricity prices, and 3) wind resources. The number of megawatts of installed distributed wind is superimposed on the combined figure, in most cases confirming the influence of these market factors. This analysis identified Indiana, Michigan, Pennsylvania, Maine, eastern Colorado, western Kansas, and northern New Mexico as areas that have untapped economic distributed wind potential. Specific policy, electricity price, and wind resource examples are provided from Iowa, New York, Washington, Oregon, and California.

This paper estimates the seasonal and zonal $${\mathrm{CO}}_{2}$$ marginal emissions factors (MEFs) from electricity production in the Italian electricity system. The inclusion of the zonal configuration of the Italian wholesale power market leads to a complete measurement of marginal emission factors which takes into account the heterogeneous distribution of RES power plants, their penetration rate and their variability within the zonal power generation mix. This article relies on a flexible econometric approach that includes the fractional cointegration methodology to incorporate the typical features of long-memory processes into the estimation of MEFs. We find high variability in annual MEFs estimated at the zonal level. Sardinia reports the highest MEF (0.7189 $${\mathrm{tCO}}_{2}$$ /MWh), followed by the Center South (0.7022 $${\mathrm{tCO}}_{2}$$ /MWh), the Center North (0.4236 $${\mathrm{tCO}}_{2}$$ /MWh), the North (0.2018 $${\mathrm{tCO}}_{2}$$ /MWh) and Sicily (0.146 $${\mathrm{tCO}}_{2}$$ /MWh). The seasonal analysis also shows a large variability of MEFs in each zone across time. The heterogeneity of results leads us to recommend that policymakers consider the zonal configuration of the power market and the large seasonal variability related to carbon emissions and electricity generation when designing incentives for renewable energy sources expansion and for achieving emission reduction targets.

Many countries have targeted a gradual transition towards 100% green generation; however, there is uncertainty concerning the economic and social consequences of such a transition. The main technologies that have been implemented are hydro, wind and solar. The latter two could cause an increase in electricity prices due to a mismatch between demand and supply. This paper uses a system dynamics approach to analyze the transition process of Switzerland, which is gradually moving from nuclear towards solar and hydro base generation. We consider hydro-pumped storage to address the timing problem between supply and demand. We developed different scenarios to test the viability of such a system. Our findings indicate that leaving the system to a free market will entail shortages during the transition, as well as a doubling of the electricity price. To mitigate this effect, we propose a capacity auction mechanism to smooth the transition process. We find that subsidizing PV indirectly encourages storage, thereby eliminating shortages, and mitigating the increase in the electricity price during the transition.

The installation of wind power technology is growing steadily and the trend can be expected to continue if the objectives proposed by the European Commission are to be achieved. In some countries a considerable percentage of installed wind power capacity is near the end of its useful lifetime. In the case of Spain, the figure is 50% within five years. Over the last 20 years, wind energy technology has evolved considerably and the expected capacity factor has improved, thus increasing annual energy production, and capital expenditure and operational expenditure have decreased substantially. This paper studies the optimal decision under uncertainty between life extension and full repowering for a generic wind farm installed in the Iberian Peninsula when the future hourly electricity prices and the capacity factor evolve stochastically and seasonally. The results show that in economic terms, full repowering is the best option, with a net present value of €702,093 per MW installed, while reblading is the second best option. The methodology can be transferred to other specific wind farms in different electricity markets and can be used to develop national wind energy policy recommendations to achieve projected shares in the electricity mix. This research was funded by the Basque Government through the BERC 2018–2021 programme and by the Spanish Ministry of the Economy and Competitiveness (MINECO) through BC3 María de Maeztu excellence accreditation MDM-2017-0714. Further support is provided by the MINECO project RTI 2018-093352-B-I00.

Australia is a world leader in household uptake of solar photovoltaic systems. In this paper, we use household-level data to identify economic, social, and environmental factors that influence actual uptake and the intention to install. We find that higher net wealth is generally associated with higher likelihood to install. Households that have mortgages, that spend more on electricity, and that pay higher average electricity prices are more likely to intend to install. Environmental preferences and related behaviours, property tenure, and space constraints are associated with both actual uptake and intention to install. We use data from the Survey of Income and Housing of 2015-16 and the Household Energy Consumption Survey of 2012.

This paper proposes a framework to address day-ahead scheduling of a microgrid (MG) with wind, solar and micro-turbine distributed generators as well as batteries, considering the associated uncertainties and AC power flow constraints. The problem uncertainties are wind and solar generations, demand, and grid electricity price. The uncertainties are modelled using bounded intervals within adaptive robust optimization method. Unlike previous robust models presented for MG scheduling, our proposed adaptive robust model optimizes both wait-and-see and here-and-now decisions simultaneously which leads to obtaining a better operating point. In order to make wait-and-see and here-and-now decisions and immunize the model against worst case realizations, the proposed MG scheduling model has been formulated as a tri-level optimization framework. In the first level, here-and-now commitment decisions are made (these are decisions made before realization of uncertainties). In the second stage, the worst case realization of the uncertain parameters is determined. In the third level, the wait-and-see recourse decisions are made (these are decisions made after realization of uncertainties). Since there are before uncertainty-realization decisions, worst uncertainty-realization determination, and after uncertainty-realization decisions, the proposed model becomes a tri-level optimization framework. The proposed adaptive robust tri-level MG scheduling model, the proposed solution approach including AdptRob and ConvAC problems, and the proposed out-of-sample analysis are the main contributions of this paper. A convexified AC power flow model is used in the proposed framework for modeling the distribution network. The 69-bus distribution test system is used to test the proposed adaptive robust model as well as the proposed solution approach which converges very fast. Moreover, the results of the out-of-sample analysis shows that the proposed adaptive robust model results in lower expected cost and lower cost variance.

Higher electricity prices following the introduction of an emissions abatement policy are generally considered a foregone conclusion, with complementary schemes or tax cuts often proposed to deal with their economic and political consequences. Rather than considering these price rises as an inevitable consequence of an emissions abatement policy, we demonstrate how wholesale electricity prices can be targeted while simultaneously placing a price on emissions. This is accomplished by implementing an output-based allocation mechanism which penalises or rewards generators based on their emissions intensity with respect to a sectoral baseline. A systematic modelling framework is presented that can be used to calibrate parameters for this scheme in order to achieve given environmental and economic objectives. Topological considerations and network constraints are captured within this framework, along with strategic interactions between a policy maker seeking to implement the scheme, and market operator responsible for dispatching generators. This highly generalisable framework is then implemented using data for the Australian system, and demonstrates how a price on emissions can be established while simultaneously minimising disruptions to average wholesale electricity prices.

The objective of the present work is to provide a comprehensive picture of the benefits coming from a wind and wave project that would operate near the largest and the most significant islands. According to the data processed in this study, the wind power has values in the range 47.1 W/m2 (Borneo) – 1430 W/m2 (Greenland), while for the wave power a minimum value of 2.28 kW/m is noticed close to Sulawesi, compared to a maximum of 68.8 kW/m encountered close to Tasmania. As a next step, the performances of several wind and wave generators were evaluated. Thus, a wind farm may reduce the emissions with a maximum 84% in the case of Madagascar, or 88% near Iceland, while from the wave converters, only the Wave Dragon may reduce about 8% in Ireland or, on the other hand 86% in Iceland. In the final part of the work, the levelised cost of energy (LCOE) was also assessed. Thus, it was estimated that for some sites the wind systems already satisfy the EU target for the year 2025 of 0.11 USD/kWh, compared to the wave systems that still have a high electricity price being quite far from these targets.

This paper proposes an analytical method that incorporates the time of use (TOU) strategy into the reliability evaluation of power system. The TOU electricity price strategy enables the savings of electricity cost, but its impact on power system reliability has not been thoroughly studied. The proposed method constructs a peak-flat-valley period partition model and develops an equal step length iteration algorithm to obtain the optimal period partition. A price-based demand response is modeled to calculate the hourly load using an apportionment method. Using the particle swarm optimization algorithm, a TOU-based optimization model with a penalty is constructed to find the optimal electricity price. Then, an analytical “electricity price–reliability” model is developed to evaluate the reliability of the power system considering TOU. The effectiveness of the proposed method is investigated through the case study on the Roy Billinton test system and reliability test system.

In this paper a robust approach to modeling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameter estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function with respect to the non-robust Least Squares estimator. Finally, the introduction of generation from renewable sources in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.

This paper simulates the real household expenditure effects of large electricity price increases in Zambia. First, we find that electricity subsidies are highly regressive. The richest household quintile receives over 60% of the subsidies, while the poorest quintile receives less than 1%. Second, our partial equilibrium simulations of the welfare effects of a 75% electricity price increase show that the poorest households would experience a 3 times larger percentage loss in real expenditure compared to the wealthiest households. Naturally, these adverse effects lead to an increase in poverty. However, we find that transferring the fiscal savings realised from the complete elimination of electricity subsidies to the poor reduces extreme poverty significantly. This budget-neutral strategy is particularly attractive for Zambia, and other sub-Saharan economies currently battling the twin challenges of widening budget deficits and high extreme poverty.

The financial and technical decisions of a smart micro-grid energy management system have always been affected by the existence of uncertainty in various parameters, such as electric load, electricity price, and renewable generation. Especially when the behavior of price and load is influenced by each other in a smart micro-grid. In this paper, we describe the uncertainty issue in the consumption behavior of price responsive loads and we utilize a model based on robust optimization to consider this uncertainty. The effects of considering the uncertainty of price responsive loads with the proposed model on a typical risk-constrained optimal operation of a smart micro-grid are illustrated through two case studies. The first case involves solving the considered optimal operation problem without considering the proposed uncertainty model while the second one shows the effects of proposed uncertainty model on the optimal solutions. The operation optimization problem is formed as a mixed-integer two-stage stochastic framework. Results show that with this uncertainty model the manager of smart micro-grid can increase its bids in day-ahead market at peak and mid-peak time slots while it can decrease them in off-peak time slots. Also, considering the impacts of this uncertainty model on profitability and system reliability, the smart micro-grid manager needs a trade-off between increasing its profit and increasing the system operation risk.

China and the United States respectively adopted desulfurization electricity price and emission trading to achieved the effective sulfur dioxide emissions reduction in thermal power industry. By the methodology of cost-benefit analysis and comparative analysis with the United States, this paper analyzed the coal-fired power plants’ desulfurization cost, benefit, and social emission reduction cost of China’s desulfurization electricity price policy. The main findings are, the results of cost-benefit analysis show that China’s policy against the polluter pays principle, polluters can get benefit from this policy. Second, in terms of the cost of emission reduction, China’s cost is very high with 5.52 billion U.S. dollars in 2000 while the USA only cost 0.5-2.0 billion US dollars. Also, compared with the decreasing social cost of the United States, the China desulfurization electricity price policy leads the social SO2 reduction cost expanded and economic inefficiencies. The conclusion is, as the policy’s objective has been achieved, the desulfurization electricity price policy in China should be cancelled. The way of reducing pollutants emission in the form of price subsidies cannot be extended to other pollutants or other industries emission control.

How to balance supply and demand have become a long-term question in the electricity market, and anomalies related to calendar issues are critical factors to affect the resource allocation. This paper introduces a test method to control all potential calendar anomalies, and assess the significance of all possible calendar effects in different time frequencies. We implement our test method to the largest electricity trading platform in the United States. Using the high-frequency intraday trading data, we assess the calendar effects in different time frequencies (Day-of-the-week, Hour-ofthe-day, Month-of-the-year, Day-of-the-month and season). Our results confirm that calendar effects exist in every dimension of time frequency, and specify those calendar effects with statistical significance. Moreover, this study discovers commonalities between electricity markets and financial markets, which makes it feasible to apply the management of financial markets to electricity markets. Besides, the detected calendar effects depict periodic patterns of market inequilibrium and facilitate the implementation of corresponding technical solutions in electricity markets.

Power-intensive processes can potentially provide significant demand response (DR) services. Modeling such processes for demand response is not trivial as models must depict plant transient properties under highly dynamic operation while remaining computationally efficient. We develop a demand response-oriented model for an important power-intensive process i.e., chlor-alkali production using membrane cells, and demonstrate the provision of fast demand response by an industrial-size plant. Through an extensive simulation and optimization case study, we show that the fast modulation of the cell power demand is possible without adverse impact on cell concentration and temperature. Additionally, the cell temperature dynamics are found to restrict the demand response capacity of the plant and must to be explicitly accounted for to support dynamic cell operation in DR scenarios. Substantial load curtailment during peak electricity price periods can be achieved and the energy cost to the electrolysis plant can be reduced.

In the process of demand response implementation, price changes will cause users to change their electricity consumption, which will cause the trend of the entire power load curve. This paper considered the sensitivity of users to electricity prices, established real-time pricing models considering price-based demand response measures and formulated the real-time pricing sale scheme. Considering the more time points of real-time electricity price, on the basis of this, ladder-type price is further introduced, and the scheme of selling electricity by ladder-type price is established. Finally, the overall benefit of the real-time pricing scheme, the ladder-type price scheme and the original time-of-use electricity price scheme are compared and the validity and feasibility of the proposed scheme are verified by the case. It is hoped that this paper can provide some references for the sellers to formulate the strategy of selling electricity in the competition of selling electricity.

The volatility of renewable energy and the time variability of load bring challenges to load forecasting. To improve the accuracy of prediction, the paper use gated recurrent unit (GRU) network to forecast short-term load considering the impact of electricity price. First, methods are proposed to group historical data based on the main features of input. Second, the rules of classification tree are established to judge which cluster the new data belongs to. The gated recurrent unit network is trained by features of input and power load from the selected group. Finally, the real-world data are used to analyses the impact of electricity price and demonstrate the validity of GRU network. The simulation study shows that the proposed approaches can improve the accuracy for forecasting short-term load and have better performance than traditional methods.

With the liberalization of China’s electricity sales side, a large number of power sales companies emerge and occupy the market, forming a pattern that many power sales companies are competing for the market. Based on the analysis of the connotation, type and purchasing and selling electricity flow of the power selling company, this paper first proposes the power user response model based on peak and valley time-sharing electricity price, then constructs the purchasing and selling electricity model of the power selling company considering differentiated time-sharing electricity price, and finally analyzes the purchasing and selling electricity model through the example. The results show that the model can optimize the load curve of power users and reduce the cost of electricity while maximizing the profit of power selling companies.

This study investigates the influence of varying financial parameters to the economic analysis of a proposed feeder, by using the sensitivity analysis which is the common methodology in order to find out the uncertainties in any assessments, the impact of specific parameters of any numerical analysis and the consequences of certain assumptions. The feeder system examined in this study will be varied in term of its PV penetration, ranged from 60% to 100%. The study’s objective is to identify the most sensitive economic variable in different economic analysis namely: interest rate of return (IRR), rate of investment (ROI) and payback period (PP), and under net present value (NPV). As the results, this study found that PV cost and electricity price are the most influencers to those economic analysis. Under IRR, ROI and PP, it is identified that PV cost predominantly affect the results of analysis. However, under NPV, the electricity price is changed dramatically than PV cost. The result of NPV changes around -72.46% and 131.90%, while the price goes down and up at 50% of its original value, respectively. In another conclusion, this study found higher PV’s penetration in the feeder system will also rise the variable’s sensitivity level, however, it is not significant enough compared with the change from every examined financial parameters by using sensitivity analysis.

This paper deals with the optimization of the daily operation of Polerood hydropower station being constructed in the north of Iran. Dynamic Programming method (DP) is applied as the preferred optimization tool owing to the fact that it guarantees the optimal solution and is applicable to the present problem. Produced profit and peak-shaving are the two objectives considered separately in this study. The results show that the optimal water management of the case study through charging and discharging the reservoir at the appropriate times led to 4% increase in the produced profit. In another part of this study, the optimal performance strategies regarding to the two objectives (produced profit and peak-shaving) are compared. The observed similarity between the two performance strategies implies the substantial dependence of the electricity price and the network demand level. The paper ends with the profitability study of the project and the sensitivity analysis of the results to various economic parameters. Article History : Received December 15 th 2017; Received in revised form April 18 th 2018; Accepted September 16 th 2018; Available online How to Cite This Article : Feshalami, B.F. (2018) Optimal Operating Scenario for Polerood Hydropower Station to Maximize Peak Shaving and Produced Profit. International Journal of Renewable Energy Development, 7(3), 233-239. https://doi.org/10.14710/ijred.7.3.233-239

The concept of the levelized cost of energy or respectively hydrogen (LCH) is frequently chosen for techno-economic analyses of energy technologies. However, the results of the LCH very much depend on the assessment perspective, which may be either private (or synonymously business) or social. The paper identifies three points as being basically responsible for differences: (1) tax deductibility of cost components, (2) external environmental costs, and (3) discounting. The objectives of the paper are to present the levelized cost of hydrogen for improved alkaline water electrolysis (AWE) and to highlight the different results from private and social perspectives. Accordingly, the methodology comprises the different LCH calculation schemes, an energy scenario focusing on electricity, a Life Cycle Assessment for environmental impacts of AWE technology, and the monetary valuation of these impacts. As a case study, we chose the production of hydrogen from advanced alkaline water electrolysis at three different sites in Europe (Germany, Austria, and Spain). The paper shows that the private and social LCH differ strongly. Inclusion of all cost components and comparatively low discount rates provoke highest LCH from social perspective. The main differences in the case study presented here are due to full accounting for environmental costs – with the global warming potential resulting from electricity generation as the main environmental cost source – and due to electricity price projections. Finally, the paper also discusses the sensitivity of LCHs with respect to parameters such as plant lifetime, monetary values of CO2, and tax rates.

This study examines factors influencing the risk perception of Internet of Things (IoT) services in the home energy management sector. We focus on investigating the effects of the various types of perceived risks and individuals’ propensities on the overall risk perception of the IoT services and explore some demographic variables. The perceived risks include perceived financial risk, perceived performance risk, perceived security/privacy risk, and perceived electromagnetic radiation (EMR) risk. The individuals’ propensities include the sensitivity to electricity price changes, environmental destruction, and new technology acceptance. As a result, this study confirms the existence of various influencing factors in the risk perception of the IoT-based home energy management services. Perceived EMR risk is the most influential one among the various types of perceived risks of the services, followed by perceived performance risk and perceived security/privacy risk. In general, experts point out cybersecurity threats as a weakness of ICT services, but users are more likely to have fear of the EMR. Moreover, sensitivity to electricity price changes and sensitivity to new technology acceptance are significant factors. This study has a significance in that we explore various factors affecting the risk perception of IoT services in the energy management sector in detail and comprehensively. Particularly, we discuss not only perceptions about the IoT services, but also individuals’ propensities. This work may contribute not only to studies on perceived risk and technology acceptance but also to stakeholders trying to market the IoT services to consumers.

Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.

We model day-ahead electricity prices of the UK power market using skew generalized error distribution. This distribution allows us to take into account the features of asymmetry, heavy tails, and a peak higher than in normal or Student’s t distributions. The adequacy of the estimated volatility model is verified using various tests and criteria. A correctly specified volatility model can be used for analyzing the impact of reforms or other events. We find that, after the start of the COVID-19 pandemic, price level and volatility increased.

The problem of wind power curtailment (WPC) during winter heating periods in China’s “Three-North regions” is becoming worse. Wind power heating, though being an effective way to increase wind power consumptions, is constrained by high electric heating costs under a peak-to-valley electricity price pattern. This study develops a real-time price (RTP) decision model which adjusts the time-varying RTPs within an acceptable range of heating users based on the WPC distribution over a particular dispatch day. The lower RTPs accompanying the higher WPC can guide the electric heating user side equipped with regenerative electric boilers (REBs) to actively increase REB imports to absorb additional wind generation. Then, the demand side response using REBs under the RTP scheme is optimized to minimize the total heating cost met by electric heating users while assisting in the large-scale wind generation accommodation. The total heating costs and WPC reductions under different heating scenarios are compared and discussed alongside the effectiveness of the RTP-based demand side management in terms of reducing the WPC and heating costs and increasing the feasibility of wind power heating during winter heating periods.

This paper proposes a multi-timescale coordinated operation approach which coordinates the combined heat and power (CHP) plant and wind farm operation in a deregulated day-ahead heat market and a real-time balancing electricity market. This approach aims to optimize profits of the CHP-wind farm portfolio by considering energy sale in the day-ahead heat market and penalty in the real-time two-price balancing market. Multiple uncertainties of heat load, wind power generation, day-ahead electricity price, up- and down-regulation prices are taken into account for the optimal operation. A stochastic optimization method is applied to solve the proposed coordinated operation problem. The proposed multi-timescale coordinated operation approach is simulated on a real-life system in Copenhagen Denmark where an energy company owns both a CHP plant and a wind farm. The simulation results verify that the proposed method can achieve a profitable operation in the markets and a highly robust operation against the multiple uncertainties.

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

We investigate the process of electricity price formation in the Swedish intraday market, given a large share of wind power in the Swedish electricity system. According to Karanfil and Li's (2017) approach, if the intraday market is efficient, with large shares of intermittent electricity in the entire electricity system, intraday prices should send signals based on scarcity pricing for balancing power. Based on this theory, we analyze Swedish electricity market data for the period 2015?2018 and find that the Swedish intraday market, despite its small trading volumes, is functioning properly. In particular, our results show that intraday price premia mostly respond to wind power forecast errors and other imbalances resulting from either supply or demand sides of the electricity market, as they should if the intraday market is efficient. The results of wind power forecast errors hold for central and southern Sweden, but not for northern Sweden where the share of wind power production is still very small. However, we find no effect of unplanned nuclear power plant outages on intraday price premia.We investigate the process of electricity price formation in the Swedish intraday market, given a large share of wind power in the Swedish electricity system. According to Karanfil and Li's (2017) approach, if the intraday market is efficient, with large shares of intermittent electricity in the entire electricity system, intraday prices should send signals based on scarcity pricing for balancing power. Based on this theory, we analyze Swedish electricity market data for the period 2015?2018 and find that the Swedish intraday market, despite its small trading volumes, is functioning properly. In particular, our results show that intraday price premia mostly respond to wind power forecast errors and other imbalances resulting from either supply or demand sides of the electricity market, as they should if the intraday market is efficient. The results of wind power forecast errors hold for central and southern Sweden, but not for northern Sweden where the share of wind power production is still very small. However, we find no effect of unplanned nuclear power plant outages on intraday price premia. ? 2021 Elsevier B.V. All rights reserved.

The purpose of this paper is to describe the upcoming changes that will bring the transition from piston engines to all-electric aircrafts. The article focuses on the differences in operation of small general aviation aircrafts. This topic is timely, as the first all-electric aircraft was certified by the European Union Aviation Safety Agency (EASA) in 2019. As there are no data concerning this new type of operation available, the data have been derived from other applicable sources. At first, we compared the energy consumption of the same aircraft with the piston engine, and then afterwards with the retrofitted all-electric variant. Our results focus on the difference in fuel price, which is discussed in the context of electricity price comparison with AVGAS prices. Moreover, we discuss the environmental impacts, especially concerning electricity source mix and emissions produced (we estimate both with and without life-cycle assessment). In the discussion, we compare the results and identify the benefits of an all-electric solution. Furthermore, several operational restrictions of all-electric aircrafts are discussed.

International audience; Commercial greenhouses in Denmark account for 0.7% of the total Danish electricity consumption in 2017. 75% of the consumption is estimated to come from artificial light. Artificial light in the greenhouses is identified as a major potential to provide energy flexibility through demand response. Energy flexibility in greenhouses is depending on the electricity price and weather conditions as artificial light is managed in relation to natural light and hourly electricity prices. Therefore, this paper investigates commercial greenho se gro ers adoption beha ior of implicit demand response enabled software solutions in different geographical regions with different weather conditions and electricity markets. The simulation of the greenhouses is developed as an agent-based system. The paper uses Denmark as the baseline model, the UK for market comparison with Denmark, and Spain and Denmark for anal ing the adoption rate s sensitivity to solar irradiation. The results reveal that higher electricity prices and more solar irradiation result in faster adoption and the adoption rate is more sensitive to electricity price than solar irradiation.

Regulated Electricity Price Components (REPCs) such as fees, taxes and levies dominate the cost structure for every consumed, stored, or even generated kWh of electricity in Germany and other European countries. This decreases financial incentives for flexible consumption, storage or generation which will be needed to handle increasing volatility in generation and demand.In this paper, we propose a linear optimization-based market matching mechanism for Local Energy Markets (LEMs) which considers REPCs. With this approach, the REPCs can be split and allocated to substations, feeders, and participants thereby incentivizing trades within close spatial proximity.We compare different schemes for spatially distributed REPCs for a demonstration scenario with 120 LEM participants with electric vehicles, battery storage and solar PV systems. Compared to a business as usual scenario, local self-sufficiency can be increased by about 14 percentage points. Additionally, peak loads during midday and evening can be decreased by 13-58 %.

Taking into account the quasi-dynamic characteristics of the thermal system can improve the complementary characteristics of the integrated energy system and reduce the operating costs. Firstly, by analyzing the quasidynamic characteristics of the heating building and the human comfort model, the virtual energy storage model of buildings is established in this paper. Then, taking the lowest economic cost of regional integrated energy system as the objective function, an optimal model for integrated energy utilization is proposed under time-of-use (TOU) electricity price, and CPLX is adopted to solve the model. Finally, the simulation results show that the proposed method can improve the economy of regional integrated energy system.

With the rapid development of electric-vehicles at home and abroad, as an electric load, electric-vehicles have an increasing impact on the power grid. Therefore, it is of great significance to study the charging optimization strategies of electric vehicles. This paper proposes an electric vehicle charging optimization strategy based on electricity price demand response. First, based on the driving characteristics of electric vehicles, the load model of electric vehicles in disordered charging mode is established. Then, considering the real-time electricity price, a multi objective charge-discharge model of electric vehicles under different weight combination modes is established. Finally, through a comparative example, the charging and discharging schemes of electric vehicles in different scenarios are analyzed and compared, and the effectiveness of the multi objective optimal control strategy for electric vehicles proposed in this paper is verified, which is conducive to coordinating the impact of load fluctuation regulation on consumer interests, to achieve optimization of multi-objective cars.

This paper provides a structured literature review of the negative economic effects associated with the development of wind energy and synthesized the evidence at an abstract level. We then developed an analytical framework to systematically review economic issues such as volatility, electricity price, housing values, and unemployment in relation to wind energy. Global wind energy development data from the time period from 2000 through 2019 were included for a more robust analysis. This period encompasses the vast majority of total global installed wind energy capacity. After amalgamating evidence from existing studies and data banks, we discuss the policy implications, suggest avenues for future research, and propose solutions to mitigate externalities. By understanding the negative economic impact created by the expansion of wind energy, we can better equip policy makers and developers to create more efficient and sustainable energy policy to benefit citizens and preserve the environment for generations to come.

The aim of this paper is to intoduce a biologically insipired methodology for energy price prediction multi-step ahead. The system combines a Genetic Algorithm (GA), Lindenmayer Systems, and Deep Neural Networks (DNNs). In the final section of the paper, we present some experiments to investigate the possibilities of the method, especially for scenarios in which DNNs should be evolved. The results for MLP networks show good ability to predict spikes and satisfactory accuracy according to error measures up to 9.8%. In 58.33% of the cases, ADEANN-Deep provides better results than other hybrid systems. Preliminary studies show that the LSTM network can predict the PLD value with a percentual error of up to 2.9%, which is lower than that obtained using multilayer perceptron networks and other methods.

This article presents and validates a Europe-wide electricity system model for techno-economic analysis. It is a zonal model in which 35 countries are modelled as individual bidding zones. Validation of the model is performed via comparing modelling results with historical records of 2016. The model replicates historical electricity system behaviour well in terms of electricity production and cross-border trading. Modelled hourly prices follow historical variations. The model results in an average annual load weighted electricity price of 35.41 €/MWh, which is 3 € less than the historical value. The presented validation exercise provides a simple and fast way to improve modelling accuracy through adjusting input data and modelling assumptions. This article also shows how to set up a complete Europe-wide electricity system model from open-source data.

Exploring the factors affecting residents' electricity-saving behavior and their mechanisms of action is an important way to conserve regional energy and reduce emissions. Integrating the theory of planned behavior (TPB) and norm activation model (NAM) and introducing the external policy factors, a model of the factors influencing habituation and investment electricity-saving behavior was constructed and an empirical study of urban residents in Hefei city, China was conducted. The results show that residents&rsquo, knowledge of the electricity price policy indirectly affects their habitual intentions by affecting their attitudes whereas residents' knowledge of the subsidy policy have direct effects on their investment intentions. Environmental concern can directly affect residents' habituation and investment intentions and also indirectly affect their habituation ones by affecting their personal moral norm. However, its indirect effect on the investment intention is not significant. Perceived behavioral control has a significant positive impact on residents' investment intentions and behaviors, but the impacts on their habituation ones are not significant. The relationships between the personal moral norm and the two kinds of intention and behavior are opposite to those of perceived behavior control. Relevant policy implications for the government, household appliance enterprises, and power enterprises are provided.