Electricity Prices Research Articles

Resilient energy management strategy in smart residential buildings considering price attack: An aggregative game perspective

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated.

Sequential pricing of electricity

This paper investigates the design and analysis of price formation in wholesale electricity markets given variability, uncertainty, non-convexity, and intertemporal operating constraints. The paper’s primary goal is to develop a framework to assess the many resource participation models, reserve product definitions, and enhanced pricing methods that have arisen in U.S. systems, especially in the context of growing contributions from wind, solar, and storage. Departing from the static models typically used for electricity auctions based on thermal resources, the paper situates price formation within the sequential decision problem faced by system operators. This more complete description of the problem has several implications for price formation. Since prices are derived from operational models, algorithmic choices in the design of policies for the sequential decision problem influence the prices ultimately formed. In numerical tests, policy variants with comparable operational performance (within 4% in terms of total cost) lead to substantial differences in prices and resource remuneration. Storage is particularly affected, earning revenues ranging from 57% to 117% of the amount suggested as economically efficient by a benchmark approximated through stochastic programming.

Is the clean energy transition making fixed-rate electricity tariffs regressive?

Wholesale electricity prices can rapidly change in real-time, yet households usually face fixed-price electricity tariffs. In markets with large amounts of solar electricity generation, households that predominantly import energy in the daytime when wholesale prices are low implicitly cross-subsidize households with energy use that is more weighted to the higher-priced evening. We map substation data on electricity use to demographic data, to identify the household characteristics associated with this cross-subsidization in a high-solar setting. We find that households in areas with low house prices and high levels of renters are the net funders of this implicit subsidy. These households currently have the lowest average energy cost for retailers to service, and could be the greatest immediate beneficiaries if real-time retail tariffs are made available, before accounting for price-responsiveness. Finally, we present evidence that cross-subsidy magnitudes have grown significantly in recent years, coincident with rapid solar generator penetration.

Analyzing the Impact of Volatile Electricity Prices on Solar Energy Capture Rates in Central Europe: A Comparative Study

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics.

Weather conditions, climate change, and the price of electricity

We estimate the effect of temperature, wind speed, solar radiation, and precipitation on wholesale electricity prices for six European countries, analyzing the full distribution of the weather variables. We provide evidence on nonlinear and extreme weather effects on electricity prices. In all countries, reductions in temperature below a certain threshold increase electricity prices, yet these thresholds tend to be lower for colder countries than for warmer ones. In addition, warmer countries have an upper threshold above which temperatures also increase prices. The precipitation threshold is near the maximum for countries with limited hydroelectric generation and much lower for others with high hydropower capacity, such as Norway. Wind speed has a similar effect on electricity prices across countries, while irradiance has a statistically significant effect in countries with the highest solar capacity and higher average irradiance. Ultimately, the impact of weather conditions on electricity prices is influenced by a country's initial climatic conditions, generation mix, policies, energy efficiency levels, and behavioral factors. Policies aimed at reducing the disproportionately negative impacts of climate change on vulnerable populations should ideally be informed by accurate quantification of the impact of weather on electricity prices.

Towards optimal customized electricity pricing via iterative two-layer optimization for consumers and prosumers

Designing an electricity price mechanism that efficiently enables users to manage their self-production and consumption behavior to accommodate renewable energy and regulate peak load is a hard task with the emergence of prosumer with photovoltaic and energy storage. To address this issue, we propose an iterative two-layer optimization to investigate customized price of electricity for different types of users in the presence of photovoltaic uncertainty. By taking into account power flow restrictions, the model is used to explore the optimal time-of-use, demand, and on-grid prices for both electricity consumers and prosumers. The consumer and prosumer peak-flat-valley periods are initially divided in the upper layer. Then, a model for distribution system operator profit maximization is proposed to minimize operation cost, power loss, and peak–valley difference. A stochastic optimization model is presented in the lower layer for consumer and prosumer to hedge against the risk of uncertain photovoltaic with the aim of minimizing electricity tariff, demand tariff, and life cycle cost of energy storage. Numerical experiments conducted on modified 15-bus and 69-bus distribution systems with different types of users show the effectiveness of the iterative two-layer optimization for customized price of electricity in peak-shaving and valley-filling while ensuring the interests of distribution system operator and users. Results show that the total profit increases by more than 4% and the net load fluctuation variance reduces higher than 20% compared with the traditional pricing method.

Transactive energy framework in fuel cell based multi-carrier energy hubs based on conditional value-at-risk

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost.

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

Robust optimization for integrated energy systems based on multi-energy trading

Amid rising energy demands and environmental concerns, integrated energy systems (IESs) face conflicting interests. Conventional strategies of interest distribution face issues such as irrational resource allocation. Accordingly, establishing energy trading strategies with multiple stakeholders becomes essential. This paper proposes a robust optimization (RO) for IESs based on multi-energy trading to reduce energy trading cost. In this regard, a single-leader-multi-follower Stackelberg game is first modeled where IES acts as the leader, and the users and the electric vehicles (EVs) act as the followers. Secondly, this model is transformed into a single-layer linear model and integrated into the multi-stage RO. With this, the nonanticipativity in the two-stage RO can be effectively handled. Besides, by constructing multi-interval uncertainty sets for renewable energy, load, and electricity prices, the conservatism of the model is reduced, making the optimization results closer to actual condition. Noteworthy that the Nash bargaining method ensures a fair distribution of benefits among IESs and encourages them to participate in energy trading. Finally, the multi-energy trading model is solved using the prediction-correction-based alternating direction method with multipliers (PCB-ADMM) algorithm. The PCB-ADMM not only protects each IES’s privacy but also has less execution time than the ADMM algorithm. The effectiveness of the proposed strategy is validated through simulation using Matlab.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

Data-driven based financial analysis of concentrated solar power integrating biomass and thermal energy storage: A profitability perspective

This study utilizes decision tree algorithms to estimate the financial feasibility of concentrated solar power (CSP). The main focus of CSP is on solar tower (ST) technology combined with two backup systems, such as biomass boilers and thermal energy storage (TES). The main goal is to develop three decision tree algorithms to predict the power plant's profitability factor (PF) for each of the following three operational scenarios: solar tower-base case-no biomass (ST-BC-NB), solar tower-operation strategy 1-medium biomass (ST-OS1-MB), and solar tower-operation strategy 2-full biomass (ST-OS2-FB). PF was predicted according to main input parameters, including direct capital costs, biomass cost, annual escalation rate, hourly electricity price, annual escalation rate, and peaks and troughs for daily electricity prices. Thermal energy storage was in five different capacities: no-thermal energy storage (No-TES), 5 h, 10 h, 15 h, and 20 h. The decision tree models demonstrated accurate predictions with low errors, high confidence levels, and most data falling within the 95% confidence interval for the "No-TES" case. Solar power plants with biomass backup had a 30% reduction in generation costs compared to conventional plants. The configurations without thermal energy storage had the highest profitability, with a maximum PF of −0.014 USD/kWh and a 25% chance of achieving profitability.

Power Interconnectivity for the Caribbean Area

Caribbean area is considered a renewable energy hub when sustainable energy sources are considered. Renewable energy sources include solar, wind, hydropower, bioenergy, and geothermal energy. The PWRLINKCAR (Power Link Caribbean) project proposes the union of the electrical markets of Greater and Lesser Antilles for promoting the use of renewable energies. In this way, the integration of the electricity markets reduces energy stocks and electricity prices. Energy link geographical analysis is performed regarding the present infrastructures and the depth of the trajectory where submarine cable will be placed. Therefore, the cost of the power link is directly related to the depth of the ocean considering submarine cable performance and the installing process. The benefits of the interconnectivity are social, political, economic, and technical.

Designing a cost-effective policy mix for transition toward net-zero emissions: a case study of the mid-term plan by 2035 of Taiwan

This study explores the design of a cost-effective climate policy mix for transition toward net-zero emissions in the mid-term (by 2035). Its novelty lies in the methodological advancements through the development of a soft-linking procedure that integrates a top-down computable general equilibrium model with a bottom-up energy system model. The integrated assessment focuses on the climate policy mix under the framework of Taiwan’s Climate Change Response Act passed in 2023. The analysis shows that a policy mix combining investments in energy transition with user-pay hybrid pricing measures can facilitate cost-effective mitigation and enhance public acceptance. The proposed hybrid pricing includes cost-based electricity pricing and a two-step carbon pricing strategy that encourages early action (before 2030) for larger emitters. Finally, we demonstrate that accelerating the transition is crucial, as it reduces the economic costs of meeting emission targets by inducing further investments in energy efficiency improvement and lowering the carbon pricing levels required to close the abatement gap.

Modelling solar photovoltaic systems on dairy farms for cost savings and GHG emission reduction

Rising on-farm electricity demand, coupled with surges in electricity prices, has increased costs associated with milk production. Additionally, the use of grid electricity with a high carbon footprint depreciates the environmental performance of dairy farming. We assessed the potential of photovoltaic (PV) systems installed on dairy parlours under different policy incentives to reduce electricity costs and the carbon footprint of dairy farms in Ireland. The HOMER Pro software was employed to model electricity consumption, generation and economic performance of four 15-year PV project scenarios for 11 Irish farms. Scenarios considering the targeted agricultural modernisation scheme II (TAMS) and the microgeneration support scheme are assessed. The results show that PV systems are a feasible option to power dairy farms when current energy prices and inflation rates are applied. Small systems eligible for TAMS grants presented an average discounted payback period of 5 years, making them a better option for farmers than larger projects, which had an average payback period of 8.5 years. The deployment of PV systems reduced the GHG intensity of electricity consumed at the farms by up to 29 %.

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

System-wide benefits of temporal alignment of wholesale–retail electricity prices

Exposing residential consumers to real-time pricing (RTP) can yield significant efficiency gains, but may also pose challenges due to the inherent complexity. Using an extensive spectrum of temporal granularities for retail electricity pricing, we analyze to what extent these capture the benefits of RTP while concurrently considering the influence of three distribution tariff types in distorting the pricing signal. Via a mixed complementarity model, we consider both operational and investment decisions at retail as well as wholesale levels, accounting for their interdependencies. Our analysis reveals three key findings. Firstly, we find that decreasing temporal granularity of retail electricity prices increases total system cost due to an increasingly inefficient generation mix, however, three-hourly or six-hourly pricing can approximate RTP benefits. Secondly, decreasing temporal granularity of retail electricity prices raises average offtake and lowers average injection prices for consumers due to changes in wholesale market prices as well as consumption patterns of consumers. Thirdly, capacity-based tariffs reduce system offtake peaks and associated price increases by incentivizing battery discharge during peak consumption while volumetric tariffs, over-incentivizing solar PV investments, result in highest offtake prices and injection peaks.

Modelling and projecting regional electricity demand for Saudi Arabia

AbstractWe address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030.

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE)

There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month.

Battery Scheduling Optimization and Potential Revenue for Residential Storage Price Arbitrage

Residential energy storage systems offer significant potential for price arbitrage by capitalizing on fluctuations in electricity prices throughout the day. This study investigates the potential revenue from optimal battery scheduling for residential storage in different north-western European electricity price zones during 2023. Using Nord Pool day-ahead prices, we applied an optimization model to determine the revenue for two types of batteries: 5 kW/10 kWh and 10 kW/10 kWh. The analysis considered battery capacity, charging and discharging efficiency, and maximum charge/discharge rates. Our results show notable variations in potential revenue across different regions, with the Baltic states demonstrating the highest revenue potential. The findings indicate that while 10 kW batteries can generate higher total revenue, 5 kW batteries are more efficient in terms of revenue per cycle. These regional disparities underscore the need for targeted incentives and policies to enhance the economic viability of residential energy storage. The research results provide valuable insights into optimizing residential battery storage for price arbitrage, offering guidance for consumers, policymakers, and energy providers to maximize economic benefits in various electricity markets.