Electricity Prices Research Articles

A consumer-satisfaction model to foster consumer participation in digital sustainable energy systems

Active consumers lie at the heart of the energy transition towards digital sustainable energy systems. With digital technologies becoming more prevalent in smart homes and factories, there exists an array of opportunities for energy consumers to play an active role in the energy system and gain economic as well as ecological benefits, e.g., by generating renewable electricity themselves. Once consumers seize these opportunities, it affects the characteristics of consumed or sold energy, which we refer to as energy features (e.g., electricity price or regionality). To foster active consumer participation, understanding consumers’ preferences regarding energy features is imperative. We survey 401 private and 40 industrial consumers in Germany and analyze consumer satisfaction building on the Kano Model. Our results reveal which energy features are most important to foster consumer satisfaction and which are less important. Thus, they provide valuable insights on how to successfully foster active consumer participation in digital sustainable energy systems.

Optimal Charging Strategy of Dynamic Electricity Price for Electric Vehicles Based on Improved Particle Swarm Optimization

Optimal Charging Strategy of Dynamic Electricity Price for Electric Vehicles Based on Improved Particle Swarm Optimization

An integrated policy approach for sustainable decarbonization pathways of energy system in a city under climate change scenarios

An integrated assessment model has been developed to estimate useful energy demand, followed by an optimization model for planning the energy supply. Four scenarios have been developed. The findings of this study highlight the impact of behavioral, climate change, and socio-economic factors on energy demand and supply. Specifically, these factors can lead to significant variations in demand, such as a more than 3.48-fold difference in cooling demand or a 1.75-fold difference in the transportation demand by cars across various scenarios.The use of a dual-fuel car and electric motorcycle is suggested in all scenarios. Waste processing has been done with a landfill in all scenarios except in the final years of the net-zero emission target scenario, where an incinerator is proposed. The supply model for electricity generation in the early years suggests the combined cycle power plant followed by solar energy at the end. However, in the low-cost electricity scenario, it is always suggested to import electricity. The sensitivity analysis showed that the emission penalty and electricity price are the most important parameters. The results of this study will help policymakers and decision-makers in energy system planning and provide researchers with appropriate information, tools, and methods for future research.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

Techno-financial analysis of 100 % renewable electricity for the south west region of the UK by 2050

In the UK, the transition to 100 % Renewable Energy Systems is a critical strategy in addressing climate change, aligning with the UK government's 2050 net-zero objective. This study explores the potential for such a transformation within the South West region of the UK by 2050. EnergyPLAN16.2 software was utilised for scenario modelling and the fmincon algorithm in MATLAB for optimisation analysis. The focus was on two scenarios involving hybrid renewable energy systems incorporating onshore wind, solar, offshore wind, and two energy storage systems which are lithium-ion battery energy storage and hydrogen fuel cell energy storage. The optimisation process entailed two stages: determining renewable energy configurations for cost-efficient coverage of annual electricity demand and computing the minimum energy storage capacity to ensure a constant power supply. The results suggest an optimal configuration of 4,460 MW onshore wind, 9,880 MW solar, 6,982 MW offshore wind, and 2,273,662 MWh of lithium-ion battery energy storage for 100 % hybrid renewable energy systems in the South West region of the UK in 2050. This configuration achieves the lowest annual cost of 3208 M£/year, with a balanced wholesale electricity price ranging from 4.3p/kWh to 6.6p/kWh and a levelized cost of energy of 6.44p/kWh. Furthermore, hydrogen fuel cell storage systems can be another alternative, however, the present study showed it is not economical enough compared to lithium-ion battery storage systems. This research broadens the understanding of hybrid renewable energy systems implementation in large regions, offering valuable insights for sustainable energy planning in the South West region of the UK.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Exploring engineering applications of two-sided partial destructive disassembly line balancing problem under electrical limiting and time-of-use pricing

With the rising demand for energy and resource shortages, disassembly as energy-intensive industry should be more attention to energy conservation. However, the non-disassemblability of components (CND) and fixed-constant disassembly costs make this process relatively expensive compared to the potential profit. This paper introduces a two-sided partial destructive disassembly model to solve problem of CND. To more accurately reflect real-world conditions, this study incorporates into the model, which was not considered in previous work. A mixed integer planning model is developed by exploring the impact of time-of-use tariffs on energy consumption and profit, and its correctness is verified in a small-scale case. Subsequently, we propose an improved water cycle algorithm (IWCA) to increase the model solving efficiency. Using non-destructive, destructive, and human-robot hybrid disassembly as benchmark cases, we compare 11 algorithms from existing research, and verify the superiority of IWCA in solving different cases. Finally, the proposed method is validated using automotive engine as an example. The results show that: (1) IWCA is superior to the four latest algorithms in solving the two-sided partial destructive disassembly line balancing problem under electrical limiting and time-of-use electricity pricing. (2) The two-sided partial destructive disassembly effectively addresses the CND issue, reducing the smoothing index by 10.24% and increasing disassembly profit by 3.19% compared to other algorithms. (3) The partial destructive disassembly is better suited for addressing issues in real production processes than non-destructive or complete destructive disassembly methods.

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.

Fuel and electricity pricing and subsidies in the GCC states: recent developments

ABSTRACT This paper discusses recent developments for fossil fuel (FF) and electricity pricing and consumption in the six Gulf Cooperation Council (GCC) states, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates (UAE), which together form the core of OPEC. These countries still subsidize FFs, albeit to varying degrees and UAE least so. The states have indicated a wish to reform their FF subsidies, which has led to a fiscal drain and lower than desired FF export revenues. Apart from UAE, none of these countries’ plans have however yet come to fruition. We discuss in some detail the political basis for such plans, and the factors making them more or less likely to be pursued and succeed. Long-term considerations, and pressures from budget balance, climate change and efficient economic allocation, all speak in favor of price reforms which will be overall beneficial to all the states. More modernistic views, taking over the countries’ political agendas, also push in the same direction. The paper also discusses similar recent developments in two comparator MENA countries, Iran (structurally most similar to the GCC countries), and Egypt (which has a more different economy and export base).

Capacity planning of wind-photovoltaic-electrolysis-battery system coupling renewable fuel synthesis

The wind-photovoltaic-electrolysis-battery (WPEB) system coupling fuel synthesis by supplying hydrogen and electricity simultaneously can reduce the carbon emission intensity of renewable fuels. However the capacity planning method is rarely reported, this work takes WPEB system coupling renewable methanol synthesis as an example. The business mode and the effect of load profile shape on the capacity configuration are first studied. Then, a two-stage capacity planning method is proposed to improve system performance: At the 1st stage, the capacity configuration is performed using 5 typical load profiles, and the case with minimum net present cost (NPC) is regarded as a pseudo-optimal solution. In the 2nd stage, the wind & solar power profile of the pseudo-optimal solution is standardized and the shape is used to build a variable renewable energy (VRE) flowing load profile as model input, the capacity planning result in the 2nd stage is regarded as the optimal case. The main conclusions are summarized below: (1) The WPEB system has five revenues from selling electricity, methanol, oxygen, CO2 trading in China certified emission reduction (CCER) market and carbon emission allowance (CEA) market. (2) Deep valley load profile obtains the lowest NPC of 2.7656 billion¥ and greatest internal rate of return (IRR) of 6.8843 % among 5 typical profiles. (3) The two-stage planning method can further decline the NPC to 2.2018 billion¥ and boost IRR to 10.5583 % by employing demand-side flexibility. (4) The sensitivity factors contributing to IRR orders from high to low are methanol price > O2 price > CO2 price of CCER > unmet H2 penalty > electricity price.

Analysis and control of demand response in smart grids: An evolutionary game method

As an effective strategy for load management in smart grids, demand response establishes a bidirectional connection between the electricity supplier and users. Based on the networked evolutionary game theory, this paper studies the demand-response issue for a class of smart grids by using the semi-tensor product of matrices. The paper proceeds as follows. (i) Considering the dynamic interactions between the supplier and users, the demand response is modeled as a heterogeneous networked evolutionary game and is expressed as dynamical form by semi-tensor product. (ii) A sufficient and necessary condition is provided to verify the convergence to a fixed point of the considered system. (iii) A feedback controller is designed to ensure the system electricity consumption and price to maintain at a desired level. Finally, an example is presented to illustrate the feasibility of the proposed method.

Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust model

The integration of renewable energy into an integrated energy system (REIES) represents a promising approach to achieving clean and low-carbon energy consumption. However, the inherent uncertainty and variability of renewable energy sources and load demands present significant challenges to the operational efficiency and stability of REIES. To address these challenges, we propose a three-stage stochastic robust optimization (TRSRO) model. This model accounts for market electricity prices, source-load scenario probabilities, and output uncertainties to optimize system configurations in REIES. It also includes the optimization of power exchanges with the regional grid and strategic operational scheduling. Initial scenarios of uncertainty variables are generated using the spectral normalized conditional Wasserstein generative adversarial network (SNCWGAN), forming polyhedral uncertainty sets. Comprehensive norm constraints are then applied to capture probability distribution uncertainties within these sets. To improve the efficiency of solving the model, we introduce a two-layer column constraint generation algorithm, considering variable alternate iterations (TLCCG-VAI). The results demonstrate that the proposed method can achieve a 12.16 % reduction in total cost compared to the baseline method. Furthermore, the TLCCG-VAI algorithm reduces runtime by 82.97 % while maintaining the same level of solution accuracy.

Storing Excess Solar Power in Hot Water on Household Level as Power-to-Heat System

PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply and demand during the day. While the netting system in the Netherlands can currently negate the economic drawbacks of this discrepancy, grid congestion and imbalanced electricity prices show that improvements are highly desirable for the sustainability of electricity grids. This research analyzes the effectiveness of a Power-to-Domestic-Hot-Water (P2DHW) system at improving the utilization of excess PV electricity in Dutch households and compares it to similar technologies. The results show that the example P2DHW system, the WaterAccu, compares favorably as a low cost and flexible solution. In particular, for twelve different households differing in size (1–6 occupants), PV capacity (2.4–8 kWp), and size of hot water storage boiler (50–300 L), it is shown that the total economic benefits for the period 2024–2032 vary from −€13 to €3055, assuming the current net metering scheme is abolished in 2027. Only for large households with low PV capacity are the benefits a little negative. Based on a multi-criteria analysis, it is found that the WaterAccu is the cheapest option compared to other storage options, such as a home battery, a heat pump boiler, and a solar boiler. A sensitivity study demonstrated that these results are overall robust. Furthermore, the WaterAccu has a positive societal impact owing to its peak shaving potential. Further research should focus on the potential of the technology to decrease grid congestion when implemented on a neighborhood scale.

Using DNPV to determine the economic viability of residential photovoltaic systems in Germany: Is the investment still worth it?

The conditions for photovoltaic investments have changed significantly in recent times. Declining subsidies and rising investment costs make these investments less attractive. In addition, electricity price uncertainty has risen sharply, making the economic benefit of using self-produced electricity from residential photovoltaic systems risky. This paper provides a novel framework to determine the financial attractiveness of such investments using the Decoupled Net Present Value (DNPV) method, which allows for a systematic and consistent consideration of the individual risks inherent in these investments. Furthermore, an extensive survey of the relevant current market parameters is performed to validate the common opinion that residential photovoltaic investments are generally advantageous. It turns out that in the current market environment in Germany, residential photovoltaic systems are not economically viable under most conditions. The system evaluated in this case study with a common size of 10 kWp leads to a negative DNPV of -1,664 €. This results in a compelling need for action for political decision-makers in order not to jeopardize the ambitious goals for the expansion of power generation from renewable energy sources.

Optimization of reversible solid oxide cell system capacity combined with an offshore wind farm for hydrogen production and energy storage using the PyPSA power system modelling tool

AbstractEight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%.

Research on charging scheduling optimization of pure electric buses considering peak-valley time-of-use electricity prices and battery loss costs

In recent years, with the accelerated progress of pure electric buses in my country, the charging scheduling and operation plan of pure electric buses urgently needs to be rationalized and scientific. This study takes the charging scheduling operation in the daily operation of pure electric buses as the starting point, and effectively describes the battery loss cost function by analyzing the optimal fluctuation range of the state of charge of the power battery of pure electric buses. Combined with the analysis of the characteristics of peak and valley time-of-use electricity prices during the charging period, the problem is refined into a pure electric bus charging scheduling decision with controllable operation completion, and the parallel machine scheduling theory is used to characterize and solve the mathematical optimization model, with the aim of minimizing the operating cost of bus companies. At the same time, an immune optimization algorithm based on random key encoding and a greedy algorithm based on the idea of avoiding peaks and filling valleys are developed to solve large-scale problems. In addition, the effectiveness of the algorithm and the applicability of the method are verified through the real bus network in Shanghai.

Evaluating the levelized costs and life cycle greenhouse gas emissions of electricity generation from rooftop solar photovoltaics: a Swiss case study

Abstract The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.

Prospects for Long-Distance Cascaded Liquid—Gaseous Hydrogen Delivery: An Economic and Environmental Assessment

As an important energy source to achieve carbon neutrality, green hydrogen has always faced the problems of high use cost and unsatisfactory environmental benefits due to its remote production areas. Therefore, a liquid-gaseous cascade green hydrogen delivery scheme is proposed in this article. In this scheme, green hydrogen is liquefied into high-density and low-pressure liquid hydrogen to enable the transport of large quantities of green hydrogen over long distances. After long-distance transport, the liquid hydrogen is stored and then gasified at transfer stations and converted into high-pressure hydrogen for distribution to the nearby hydrogen facilities in cities. In addition, this study conducted a detailed model evaluation of the scheme around the actual case of hydrogen energy demand in Chengdu City in China and compared it with conventional hydrogen delivery methods. The results show that the unit hydrogen cost of the liquid-gaseous cascade green hydrogen delivery scheme is only 51.58 CNY/kgH2, and the dynamic payback periods of long- and short-distance transportation stages are 13.61 years and 7.02 years, respectively. In terms of carbon emissions, this scheme only generates indirect carbon emissions of 2.98 kgCO2/kgH2 without using utility electricity. In sum, both the economic and carbon emission analyses demonstrate the advantages of the liquid-gaseous cascade green hydrogen delivery scheme. With further reductions in electricity prices and liquefication costs, this scheme has the potential to provide an economically/environmentally superior solution for future large-scale green hydrogen applications.

Analysing the impact of diverse factors on electricity generation cost: Insights from Sri Lanka

The primary responsibility of a power system utility is to provide continuous, high-quality electricity at a fair and competitive price. To maintain low generation costs while utilising diverse energy sources, optimisation methodologies are crucial. Water value and generation costs exhibit significant variability on seasonal, daily and timely basis and reflect dynamic interactions between electricity demand and power plant availability. Currently, power system planners often overlook these seasonal and time-of day dependent variations in water value and generation costs. This research paper addresses this gap by analysing how fluctuations in electricity demand and power plant availability affect generation costs in the Sri Lankan power system. It introduces a novel economic dispatch model and presents three methods for estimating water value to calculate seasonal and time-of-day electricity costs. The discussion highlights how these findings can utilise policymaking, particularly in the context of electricity pricing debates and water management for agriculture versus electricity generation, as well as in calculating the water value for each reservoir.

On ways to reduce the energy supply costs in Russia

Purpose: is to research of ways to reduce the energy supply costs for Russian consumers.Methods: the analysis of the transformation of the electric power industry was carried out using the tools of tectology and system economic theory.Results: the key reason for the constant increase in the cost of energy supply has been identified, which is the transition from a systematic approach to energy development aimed at increasing general economic efficiency to maximizing the economic performance of independent energy companies. The need to adjust the basic principles laid down in the concept of electric power industry development is substantiated.Conclusions and Relevance: the result of the successful completion of the reform of the Russian electric power industry is a steady influx of significant investments into the industry over the course of decades. But despite the increase in power system capacity, which is outstripping the dynamics of electricity demand, the need for the most expensive peak power sources and energy storage systems is growing, which leads to an increase in electricity prices in addition to the investment component. A positive feedback has been identified that reduces the structural stability of the energy sector, namely: between rising electricity prices; reduction of electricity consumption from the network as a result of the installation of their own generation by some consumers; increasing costs of generating and network companies; more intensive growth in electricity prices and increased motivation to install their own generation for that part of consumers who continue to use grid electricity. The ways are proposed and the solutions are considered aimed at forming new connections between participants in the single technological process of production, transmission and consumption of energy resources, with the aim of reducing the energy supply costs and improving the specific performance indicators of the energy system, and in the future reducing the costs of integrating renewable energy sources into it.