Electricity Procurement Research Articles

Community Choice Electricity Aggregation and Solar Adoption: Evidence From Illinois

We explore whether municipalities voting on the community choice aggregation (CCA) model of electricity procurement relates to the adoption of small-scale distributed solar in the US state of Illinois. Municipalities that held a referendum for CCA were more likely to have small-scale solar installations compared to those that did not. Among municipalities with solar installations, those that held CCA referendums have lower per capita solar power generation than those without CCA referendums. The impacts of CCA referendums on small-scale solar adoption are mixed and should be combined with other strategies to promote broader solar adoption in the U.S. energy market.

Exploring the effect of Guarantees of Origin on the decarbonization of corporate electricity procurement: A case study of Germany and Norway

AbstractIncreasing social, economic, and political pressure causes many companies to pledge to decarbonize. A common measure involves the use of Energy Attribute Certificates (EAC), such as the European Guarantees of Origin (GO), to reduce emissions from electricity procurement (Scope 2). However, previous studies find no effect on additional renewable energy capacity. Focusing on Norway and Germany as dominant contributors to net GO exports and imports, this study examines the GO trade alongside corporate carbon accounting data to answer the research question: Does the decarbonization of corporate electricity procurement using Guarantees of Origin contribute to the expansion of renewable electricity generation capacity in Norway and Germany? The analysis of CDP and Association of Issuing Bodies data reveals Norway's consumption mix is more carbon intensive than Germany's because Norway exports GO and imports fossil electricity attributes. German companies report predominantly market‐based approach, mainly using green tariffs and GO for zero emission claims, while Norwegian companies favor the location‐based approach. The largest share of GO issued in Norway comes from hydropower plants aged 41 to 70 years. The results highlight the urgency to revise corporate carbon accounting standards. GO lack additionality due to double counting of renewable attributes. Potential solutions include additionality criteria, GO trade restrictions based on physical capacities, and age limits for eligible power plants. This study's novelty is linking CDP data with GO trade, to assess the integrity of corporate decarbonization strategies. It contributes valuable insights to ongoing discourse on the role of EAC.

Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model

Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.

On market clearing of day ahead auctions for European power markets: Consumer payment minimisation versus social welfare maximisation

For inflexible demand and considering network constraints, we introduce a Consumer Payment Minimisation (CPM) market clearing mechanism and compare it with the standard Social Welfare Maximisation (SWM) used in European Day Ahead Electricity Markets. CPM presents a challenging optimisation problem, which we addressed by four numerical algorithms with varying computational costs and convergence guarantees, evaluated on synthetic data. We derive strong, partial analytical results and the concept of optimal zonal stack curves to facilitate efficient CPM solutions. Evaluations using 2019–2020 data from the Central Western European Day Ahead Power Market under the assumption of truthful bidding show that SWM models historical prices well, while CPM reduces producers’ market power and decreases zonal prices and total electricity procurement costs compared to SWM.

Concept and Modeling of Participation Opportunity for Large-Scale Renewable Energy Plants

Citizen participation in the energy transition is desirable for many reasons: it increases acceptance of renewable energies, fulfils the wish to make one's own contribution to the energy transition and generates relevant parts of the necessary capital for the transformation of the energy system. This paper presents a scalable participation concept for the German legal framework which allows citizens to invest in and partially consume electricity from large-scale renewable energy plants at the time of generation. The developed concept provides a method for direct financial involvement via a direct contract with local electricity suppliers and virtual self-consumption by matching demand with PV generation – without the need for property ownership or new subsidies. Different consumer types and participants shares of a large-scale PV plant are modelled and simulated for the years 2019-2021. The financial advantage of citizen participation depends on the type of energy generation plant, yearly (solar) yield, on the remuneration for the surplus energy quantity, on the electricity procurement costs, on the customer's energy consumption, on the amount, and time flexibility and others. This paper shows that all analysed consumer types have the potential to profit from the model every year, although simulations are dependent on variable annual factors and assume the energy supplier's short-term procurement. The approach’s scalability and organizational simplicity could expedite the development of local participatory projects and contribute significantly to Germany's energy transition.

Research on Intelligent Scheduling Strategy for Electric Heavy Trucks Considering Photovoltaic Outputs

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy.

ANALISIS STRUKTUR PEREKONOMIAN DAN PERTUMBUHAN EKONOMI DI KABUPATEN BANYUMAS

Economic growth is the process of changing the condition of a country continuously towards the better during a period of time. The existence of economic growth is an indication of the success of economic development. The objective of this research is to find out the contribution to the development of pre-economic structures and economic growth in Banyumas district in 2021 and 2022, to know which sectors are the basis and non-base sectors in the economic structure development and growth of Banyumas district, to know the changes in the superior sector in the structure of the economy of Banyumas District. This research uses secondary data such as Gross Regional Domestic Product (GDP) data, and the rate of economic growth of Banyumas district. The analysis method used is Location Quotient (LQ), Shift Share (SS). From the results of the analysis of the calculation of the LQ sectors that have a value of LQ > 1 i.e. the sector of Processing, Construction, and Procurement of Electricity and Gas, and the sector that has a LQ < 1 i.e. agriculture, Forestry and Fisheries, Mining and Demolition, as well as Information and Communications, from the result of the shift share (SS) indicated that pointing out that there are some sectors that have the potential to be positive

ANALYSIS OF MINING, AGRICULTURE AND SERVICES SECTORS AS LEADING ECONOMIC SECTORS IN BANYUWANGI DISTRICT

This research aims to analyze various leading sectors in Banyuwangi district to be developed in order to increase regional economic growth. This research uses a qualitative approach with more emphasis on the qualitative form of literature, namely the main source from the Banyuwangi Regency Central Bureau of Statistics. The data analysis tools used are LQ, DLQ and Shift Share and the research results obtained show that in Banyuwangi Regency the most superior sector is the Education sector, namely obtaining average data of 2.02, the Electricity Procurement Sector and the Education Services Sector in Banyuwangi Regency have a very large role, this can be seen in the contribution of Electricity Procurement and the Education Services Sector to the GRDP of Banyuwangi Regency. The magnitude of the sector's contribution can be seen in the average contribution figures from 2018 to 2022 with values ​​of 2.00 and 2.02. while the Mining and Quarrying Sector, electricity and gas procurement, transportation and warehousing, accommodation provision, information and communication, financial and insurance services, real estate, government administration are sectors that are included in the Potential category.

Real-time energy management strategy of the hydrogen-coupled microgrid based on Informer model prediction results and deep reinforcement learning

Abstract Microgrids (MG) powered by emerging distributed energy resources (DERs) are increasingly pivotal in driving the decarbonization of the power system. Due to the uncertainty of renewable energy sources, electricity generated from wind and solar is typically stored, either in batteries or in the form of high-pressure hydrogen gas within hydrogen storage tanks to meet the refuelling requirements of fuel cell vehicles (FCVs). For FCVs, the substantial operational expenses associated with hydrogen refuelling stations pose a significant barrier to their widespread adoption, with a notable portion of these costs attributed to hydrogen transportation. To address this challenge, this paper employs the Informer model based on a transformer for day-ahead prediction of photovoltaic power generation, as well as load demand forecasting. Subsequently, the Twin-delayed deep deterministic (TD3) policy gradient algorithm is used for real-time energy management of the microgrid. While ensuring that the State of Charge (SOC) remains above 45% daily to meet future hydrogen demands, the MG pursues an optimal strategy to minimize the total daily cost, including electricity procurement, carbon emissions and equipment degradation. Compared to outcomes derived from rule-based approaches, the algorithm introduced in this paper minimizes solar waste and equipment degradation, facilitating hydrogen production during low-cost electricity periods and enhancing economic feasibility.

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

Green Ammonia Production in Stochastic Power Markets

Real assets in the energy market are subject to ecological uncertainty due to the penetration of renewables. We illustrate this point by analyzing electrolyzers, a class of assets that recently became the subject of large interest, as they lead to the production of the desirable green hydrogen and green ammonia. The latter has the advantage of being easily stored and has huge potential in decarbonizing both the fertilizer and shipping industries. We consider the optimization of green ammonia production with different types of electricity procurement in the context of stochastic power and ammonia markets, a necessary assumption to translate the features of renewable, hence intermittent, electricity. We emphasize the importance of using stochastic prices to model the volatile nature of the price dynamics effectively, illustrating the project risks that hedging activities can mitigate. This study shows the pivotal role of flexibility when dealing with fluctuating renewable production and volatile electricity prices to maximize profits and better manage risks.

A robust system model for the photovoltaic in industrial parks considering photovoltaic uncertainties and low-carbon demand response

Against the backdrop of carbon peaking and carbon neutrality initiatives, industrial parks have the potential to mitigate external electricity procurement and reduce carbon emissions by incorporating photovoltaic and energy storage systems. However, the inherent unpredictability in photovoltaic power generation poses notable challenges to the optimal planning of industrial parks. In light of this, the present study proposes a robust planning model for the distribution of photovoltaic and energy storage systems within industrial estates, taking into account uncertainties in photovoltaic output and low-carbon demand response. The primary objective of the model is to minimize the yearly comprehensive cost of the industrial park. It is grounded in the carbon emission flow theory, utilizing dynamic carbon emission factors calculated throughout the year as the pricing basis for real-time electricity rates informed by demand response. Subsequently, historical photovoltaic output data are employed to formulate typical output scenarios and their probability distributions through scenario clustering. These norms and constraints serve to bind the associated uncertainty probabilities. Consequently, a two-stage distribution robust model for the photovoltaic and energy storage system is established, employing a data-driven methodology. The efficacy of the proposed model is substantiated through a case simulation of an industrial park utilizing the CPLEX commercial solver. This approach not only underscores the importance of addressing uncertainties in photovoltaic power generation for industrial park planning but also showcases a practical application of the developed model.

Temporal regulation of renewable supply for electrolytic hydrogen

Electrolytic hydrogen produced using renewable electricity can help lower carbon dioxide emissions in sectors where feedstocks, reducing agents, dense fuels or high temperatures are required. This study investigates the implications of various standards being proposed to certify that the grid electricity used is renewable. The standards vary in how strictly they match the renewable generation to the electrolyser demand in time and space. Using an energy system model, we compare electricity procurement strategies to meet a constant hydrogen demand for selected European countries in 2025 and 2030. We compare cases where no additional renewable generators are procured with cases where the electrolyser demand is matched to additional supply from local renewable generators on an annual, monthly or hourly basis. We show that local additionality is required to guarantee low emissions. For the annually and monthly matched case, we demonstrate that baseload operation of the electrolysis leads to using fossil-fuelled generation from the grid for some hours, resulting in higher emissions than the case without hydrogen demand. In the hourly matched case, hydrogen production does not increase system-level emissions, but baseload operation results in high costs for providing constant supply if only wind, solar and short-term battery storage are available. Flexible operation or buffering hydrogen with storage, either in steel tanks or underground caverns, reduces the cost penalty of hourly versus annual matching to 7%–8%. Hydrogen production with monthly matching can reduce system emissions if the electrolysers operate flexibly or the renewable generation share is large. The largest emission reduction is achieved with hourly matching when surplus electricity generation can be sold to the grid. We conclude that flexible operation of the electrolysis should be supported to guarantee low emissions and low hydrogen production costs.

Non-cooperative game-based electricity pricing method for data centers

Data centers are characterized by high energy consumption, with operating costs being extremely sensitive to electricity prices. Therefore, modern data centers are often equipped with microgrids for power supply, which adjust their operational strategies based on the given electricity prices to minimize costs. However, existing research has overlooked the flexible pricing potential of electricity retailers, prompting this study to propose a non-cooperative game theory-based optimization method for data center electricity procurement negotiation. A two-layer optimization model is established for data center electricity pricing. The upper layer focuses on electricity price optimization, modeling price negotiation as a Stackelberg game and adopting a weighted average cost approach to describe the electricity procurement prices. The lower layer addresses data center operational optimization, formulated as a nonlinear programming problem. To enable rapid solution convergence, a hybrid problem-solving method combining a genetic algorithm and branch-and-cut algorithm is proposed. Finally, a simulation is conducted using a data center located in California, United States, to validate the proposed method. The results demonstrate that the proposed method can reduce data center operational costs by 3% and increase the revenue for electricity retailers by 17%, achieving a win–win outcome.

System-level impacts of voluntary carbon-free electricity procurement strategies

System-level impacts of voluntary carbon-free electricity procurement strategies

Optimal Electricity Procurement Enabled by Privacy-Preserving Samples

Optimal Electricity Procurement Enabled by Privacy-Preserving Samples

Optimal Medium-Term Electricity Procurement for Cement Producers

Optimal Medium-Term Electricity Procurement for Cement Producers

Decarbonisation strategies for manufacturing: A technical and economic comparison

Decarbonisation strategies for eight German manufacturing companies of different industries (electrical, chemical, beverage, mechanical engineering, automotive, polymer processing) are developed and analysed. The abatement potential and economic feasibility of strategies depend to a large extent on individual preconditions e.g., the area available for renewable energy systems, and dynamic effects, for example the changing emission intensity of local electricity generation. The strategies lead to a reduction of between 41 % and 88 % for seven of the eight companies.The in-depth case studies show that energy efficiency measures and expanding the company's own renewable energy plants, except for solar heat, are particularly cost-effective, while shutting-down existing combined heat and power plants has the highest abatement costs between 169 and 1686 EUR/tCO2e. Decarbonising electricity procurement and switching from natural gas to biomethane have the highest greenhouse gas abatement potential in Scope 1 and 2.The novelty of this work is the linking of economic and ecological considerations of measures in the context of company-specific decarbonisation strategies. Using marginal abatement cost curves, different options for emission reductions are discussed and general conclusions for the development of decarbonisation strategies in the manufacturing sector are drawn.

A risk-averse two-stage stochastic model for optimal participation of hydrogen fuel stations in electricity markets

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed.