Pool Market Research Articles

Fairness in energy communities: Centralized and decentralized frameworks

This research discusses fairness in energy communities while investigating two types of organizations for operation and cost-sharing. On the one hand, centralized architectures consist of operating community assets in a coordinated manner with a central controller before a community manager shares the overall benefits between the users. Four sharing strategies are investigated and implemented in a monthly post-delivery phase. In contrast, in decentralized architectures, each user operates its assets independently. In such frameworks, the costs/benefits are usually shared among users through market-based mechanisms that rely on users' bids. This work then explores the Pool market and Peer-to-peer transactions to investigate the impact of different bidding from the users' perspective. Ultimately, all the proposed centralized and decentralized approaches (10 in total) are assessed based on economic performances at both users' and community levels. Specific attention is paid to fairness within the community, which is challenging. Three indexes derived from economy and game theory are then considered, along with metrics tailored for energy communities. Results from a seven-user community indicate that the pool market systematically returns considerable savings among decentralized frameworks compared to peer-to-peer markets. More importantly, centralized frameworks systematically yield the most significant bill reduction (16 %) and fairer cost allocation compared to decentralized frameworks.

Developing Managerial Framework to Cultivate Critical Skills in The South African Automotive Retail Industry

The purpose of this study is to develop managerial framework that could be utilised to cultivate critical skills in the automotive retail industry. This would be achieved by providing a comprehensive literature overview regarding skills development strategies including life-long learning, apprenticeship, and mentorship programmes. The study utilised a qualitative research methodology. Data are collected through face-to-face interviews using an interrogation technique. Data collection process was done in participants’ natural settings, enabling the researcher to develop new knowledge, and understanding of strategic tools that can be utilised to promote skills development of technicians for the automotive retail industry. The findings reveal a strong collaboration between non-technical and technical high schools as well as technical colleges is essential to ensure the quality of skills development in the automotive retail industry is improved, and thereby improve the market pool of skilled young technicians.

Why Individuals Do Not Visit a Destination? The Role of Familiarity and Novelty Seeking in Shaping Non-visitors’ Destination Image

Non-visitors constitute a large but underexplored market pool with strong potential for tourist destinations. Drawing on the mere exposure and motivation frameworks, this study explores image differences among two groups of non-visitors (i.e., uninterested, unable to visit), along with the underlying factors for such variations. Findings drawn from two studies suggest that the groups significantly differ in terms of their images, novelty, familiarity, and intentions toward a destination. For the uninterested group it is familiarity, along with cognitive and affective image that shape intentions; while novelty and familiarity are the key determinants of conative image for those who cannot visit. The study contributes to the tourism marketing literature by demonstrating clear differences on destination image and its determinants across the two groups of non-visitors. In practical terms, such knowledge proves prudent in cultivating a place’s novelty, familiarity and desirability, thereby increasing the possibility that more non-visitors turn into goers.

Local labor market and corporate investment

To capture local labor market pooling in agglomeration economics, we employ segment information and occupation statistics to construct firm-pair labor force similarities. Our findings indicate a positive relation between local labor market thickness and corporate investment, influenced by both employer-driven labor demand and employee-driven labor supply. The findings are more pronounced in firms with more skilled labor, less routine-task labor, and higher product and technology competitions. Firms in thicker local labor markets also display higher investment efficiency, higher operating efficiency, and higher valuation. To mitigate the endogeneity concern, we employ an instrumental variable approach to show robustness. Overall, we uncover a specific linkage between the local labor market and corporate investment.

Assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods

This document presents a solution for an assignment related to the Energy Markets and Regulation curricular unit of PDEEC. The assignment consists of assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods. To check the technical feasibility of implementing the dispatch recommended by the pool market, a DC power flow is analyzed, by accounting for a network with six busbars. Results show that in some periods of higher demand, there could be an overload in some transmission lines of the considered network for certain results of market dispatch.

Blockchain-Enabled Secure and Authentic Nash Equilibrium Strategies for Heterogeneous Networked Hub of Electric Vehicle Charging Stations

In the networked enlarged electric vehicle (EV) charging infrastructures, the security and authenticity of the stakeholders involved in the EV energy market pool are prime important. This paper proposes an EV network hub (EVNH) comprising electric vehicles, electric vehicle aggregators (EVAs), and charging nodes in the networked EV energy market pool. The various EVAs implement different heterogeneous blockchains. The EV network hub facilitates blockchain-based secure and resilient energy trading under the grid to vehicle and vehicle to grid. The paper emphasizes interoperability challenges involving different blockchains to communicate and transfer assets or data between them. We suggest secure and trustworthy energy trading across various EVAs using multiple EV tokens for EV energy trading through cross-chain communications. The EVAs consider a Nash equilibrium-seeking strategy to find the Nash equilibrium in the non-cooperative game of EVAs. The effectiveness of the proposed EV network hub is tested using MATLAB, Solidity, and Python software.

Enhancing target benefits of power system stakeholders: A columns and constraint generation-based power-to-gas linked economic dispatch

Economic dispatch (ED) plays a crucial role in optimizing power plant output to minimize the cost of electricity production and ensure economic efficiency in complex power systems. However, with the growing emphasis on carbon neutrality policies and the increased use of renewable energy sources (RES), traditional ED models may lead to economic losses for independent power producers (IPP) and renewable energy providers (REP). To address this challenge, a new ED model is proposed in this study, which integrates power-to-gas (P2G) technology and takes into account the perspectives of all stakeholders, including independent system operator (ISO) IPP, and REP. The proposed approach is tested in the context of the cost based pool (CBP) market structure in S. Korea, where a mixed integer linear programming (MILP) problem is solved for each target benefit using columns and constraint generations (C&CG). By ensuring the target benefits of each stakeholder group, this improved ED model aims to encourage their participation in the power system and support economic efficiency. The simulation results of the P2G-linked ED proposed in this paper verify that as the capacity of the integrated P2G increases, there is an increase in the improvement rate of each target benefit for ISO, IPP, and REP.

A PSO-based Approach to Determine the Market Clearing Price by Considering System Losses and Reactive Power Constraints in a Pool Market

A PSO-based Approach to Determine the Market Clearing Price by Considering System Losses and Reactive Power Constraints in a Pool Market

Integrating hydro and wind resources for effective congestion management in a hybrid electricity market

Congestion management in deregulated electricity systems threatens system security and reliability. Integration of renewable energy sources, unpredictable wind power, load demand, and the requirement for quickly deployable reserves make congestion management more difficult. While deciding the dispatch of the generating units, many factors have to be considered for the economic, secure, and reliable operation of the power system. This study presents a congestion management strategy that has been developed specifically for hybrid power systems. Within a pool and bilateral power supply market structure, it introduces a generator rescheduling-based congestion management technique. Ensuring safe bilateral transactions between these companies is crucial. Renewable sources affect congestion management dynamics, according to the research. The optimization issue includes operational limitations for scenarios with and without renewable source units. Monte Carlo simulation (MCS) was used to sample hourly wind speed from the Weibull PDF wind model. GAMS CONOPT solver used the model. General Algebraic Modeling System (GAMS)-MATLAB interface imported the model into MATLAB to extract the response. The best combination of renewable sources is 2 hydro + 1 wind unit as savings of congestion cost are $1403.6/h and $1855.18/h in case 1 (3-line congestion) and case 2 (2-line congestion), respectively. Results were accomplished by updating the IEEE-24 bus reliability test system with hydro and wind generators.

A stochastic analysis of the energy and reserve operation for battery storage-assisted prosumer aggregator in the Southwest Power Pool Market

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Bi-Level Emission Reduction Model of the Hybrid Power Market Based on Carbon Emission Flow Theory and Source–Load Coordination

Limited by the influence of network topology and other factors, the theory of carbon emission flow is unreasonable in the allocation of carbon responsibility on the user side, which leads to the low enthusiasm of users to respond to emission mitigation. The emergence of bilateral transactions provides users with the freedom to choose the type of power supply which is of great significance to exploring the potential of users to reduce emissions and promote the consumption of new energy. For this reason, this paper proposes a bi-level emission reduction model of the hybrid electricity market considering carbon emission flow and source–load coordination. The upper level aims to maximize the revenue of wind, photovoltaic, and thermal power generators and establishes a market-clearing model based on the trading rules of the hybrid electricity market to obtain the bid-winning power of each generator and the hybrid market electricity price. After the market is cleared, the carbon emission liability of the user side is calculated by using the carbon emission flow theory. The lower level takes the minimum cost of electricity consumption as the target and uses electricity price and carbon responsibility as incentives to establish a decision-making model for users to purchase electricity and guide users to actively choose green energy for consumption. The results of the example show that compared with the single pool market trading model the carbon emissions of the system are reduced by 11.9% while the income of the new energy power generations is increased by 9.84% and the electricity cost of the user is reduced by 21.2%, which underlines a mutually beneficial outcome for all stakeholders in the market.

Local labor market and the cross section of stock returns

Local labor market pooling is a crucial benefit in the economics of agglomeration (Marshall, 1890). To capture the pooling function, we employ segment information and occupation statistics to capture average pairwise labor force similarities for each focal firm. We find that firms in thicker local labor markets are underpriced and display significantly positive stock returns afterwards. The return predictability of local labor market measure is concentrated in non-mega cap firms; high-tech firms; younger firms; and firms with more high-skilled labor, less routine tasks, and lower labor intensity. Our results are robust to a battery of control variables and alternative measure specifications.

The economics of renewable energy portfolio management in solar based microgrids: A comparative study of smart strategies in the market

In this study, an energy-management approach based on digital twin is developed to manage a microgrid (MG) based on the energy generated by renewable sources of solar and wind, diesel generators, energy storage, and a variety of loads consumers in the market. The MG is assumed to participate in the pool market and schedule its controllable sources accordingly as a means of maximizing profits. This paper proposes a risk-limited scenario-driven stochastic programming strategy that uses the conditional value at risk approach for handling different types of uncertainty. Double stochastic optimization levels are used to solve the proposed scheme. The day-ahead market can be optimized at one level by submitting the best bids at hourly intervals based on forecasts. As a second level of optimization, scenario-driven stochastic information has been used for determining the best schedule. Also, shark optimization algorithm is proposed to solve the optimization problem. Aside from being advantageous for MGs and their users, the suggested energy management system likewise takes advantage of the MG's aggregators and non-real power plants. This model is shown to be valid based on the outcomes and would be beneficial for the market owners.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Two-stage stochastic-based scheduling of multi-energy microgrids with electric and hydrogen vehicles charging stations, considering transactions through pool market and bilateral contracts

In order to mitigate greenhouse gas emissions and improve energy efficiency, sustainable energy systems such as multi-energy microgrids (MEMGs) with the high penetration of renewable energy resources (RES) and satisfying different energy needs of consumers have received significant attention in recent years. MEMGs, by relying on renewable resources and energy storage systems along with energy conversion systems, play an essential role in sustainability of energy supply. However, renewable energies are uncertain due to the intermittent nature of solar and wind energy sources. Thus, optimal operation of the MEMGs with the consideration of the uncertainties of RES is necessary to achieve sustainability. In this paper, risk constrained scheduling of a MEMG is carried out with the presence of the PV, wind, biomass, electric vehicles (EVs) and hydrogen vehicles (HVs) charging stations, combined heat and power (CHP), boiler, hydrogen electrolyzer (HE), cryptocurrency miners (CMs), electrical, thermal and hydrogen storage systems, responsive demands. From the trading and business model side, the proposed MEMG optimized operation relies on bilateral contracts between producers and consumers and pool electricity markets. A two-stage stochastic programming method is used for considering the uncertainties of electrical, thermal and hydrogen demands, EV and HV charging stations load, CM load, PV and wind power, and the price of electricity purchased from the pool market. The proposed mixed integer linear programming (MILP) model is solved using the CPLEX solver in GAMS which guarantees to achieve a globally optimal solution. The results show that due to the certain prices of bilateral contracts, the possibility of transaction by bilateral contracts decreases the risk metric CVaR by 50.42%. The simulation results demonstrate that risk of high operation costs while considering flexibility sources, such as storages and demand response (DR) programs, is decreased by 5.45% and 4.6%, respectively. As far as operation costs are concerned, results reveal that using renewable resources decreases operation costs by 34.47%. Moreover, the operation cost is reduced by 5.94% and 4.57% in the presence of storage units and DR programs, respectively. In the same way, storages and DR programs decrease cost of purchased electricity by 13.47% and 14.46%, respectively.

Smart inverter and battery storage controls to reduce financial loss due to overvoltage-induced PV curtailment in distribution feeders

The rapid growth of rooftop solar photovoltaic (PV) systems in low-voltage distribution networks has caused reverse power flow leading to voltage rise. As the voltage level increases, PV inverters first reduce the output power to regulate the voltage and may eventually shut down if the voltage level remains above the permissible limit. When this happens, the PV output power is curtailed, leading to financial loss. This paper examines two control strategies to reduce PV curtailment: (1) smart PV inverters and (2) residential battery storage system optimally sized to reduce the cost of household energy. Smart PV inverters can reduce the voltage level by absorbing reactive power at the expense of curtailing its active power output. Residential battery storage can reduce the voltage level by storing excess PV power instead of exporting it to the grid Using these two strategies, a systematic approach is presented to reduce PV curtailment and the associated financial loss. Three types of electricity tariffs are used, namely, electricity market pool price, and retail flat and time of use tariffs for household customers. An 11-kV distribution feeder in South Australia is analysed with the prevailing feeder and inverter voltage limits per Australian regulatory standards. The results demonstrate that installing 100% of PV systems with smart inverters and optimally sized battery storage can reduce the PV curtailment and the corresponding financial losses by 47%.

Pricing and Simulating Energy Transactions in Energy Communities

Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors’ experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities.

Forward contract prices of electricity Nord Pool market: calibration and jump approximation

Forward contract prices of electricity Nord Pool market: calibration and jump approximation

A Hybrid Machine Learning Approach for Price Forecasting in Electricity Market with Smart Bidding Strategies and Wind Energy Influence

The price for renewable power has seen a dip in recent years with increased advanced approaches in the electricity market. For the benefit of both the producers and the consumers of electricity, a forecasting technique to predict the price for the next day is required to handle competitive market participation facilitating the prosumers. Therefore, the study’s main objective is to model a day-ahead electricity price and apply a forecasting tool based on time series analysis, promising accuracy, and minimal average forecast errors. Day-ahead real-time price data of the Nordic pool market forecast study for a period between January 2019 and March 2021 is considered for the study. The impact of wind energy production on day-ahead electricity Nord Pool electricity market for improved participation in the market is forecasted based on time series. Machine learning techniques, namely Support Vector Regression (SVR), Generalized Autoregressive Conditional Heteroskedastic (GARCH), Autoregressive Integrated Moving Average (ARIMA), Multiple Linear Regression (MLR), and hybrid of SVR-GARCH are analyzed in detail to find the best forecasting approach. The proposed forecasting technique on next-day electricity price series influenced by wind power production is validated using performance indices, namely, the Mean Absolute Percentage Error and the Root Mean Squared Error considered for checking the reliability of the proposed new model. A similar study on the Indian market was also conducted to validate the robustness of the model. The electricity price forecasting model is developed and analyzed using a machine learning technique in Python (Jupyter Notebook).

Study on the economics of wind energy through cryptocurrency

The Green Pact signed by the European Union establishes a trend towards renewable energies to combat the greenhouse gas emissions. Among the technologies used to produce this type of energy, wind power generation technology stands out, which, in countries such as Spain, already has significant installed power. The main problems posed by this technology plans are the uncertainty intervals of wind power and its inclusion in the electricity market, due to the complex price system that does not always favor the producers. The main purpose of this research is to promote the installation of more wind power plants. For this, the installation of cryptocurrency mining equipment is proposed, which will be powered by the generation produced by these wind power plants. The article analyzes the production of cryptocurrencies is a growing business. In the research process, the latest cryptocurrency mining equipment is evaluated. It is analyzed which equipment is the most suitable for its installation in the wind power plant and an economic study is made for the construction project of a large wind power plant. Finally, it will be seen that in this way the amortization time of the facilities decreases and also the project is more attractive for the investor since they can decide between injecting energy into the electrical network or mining cryptocurrencies. If a wind power plant invests in cryptocurrency mining in parallel to the production of electrical energy for the grid, it can decide when to enter the electricity market pool or engage in mining. In this way, the idea of building many more wind power plants becomes more attractive. This would lead to a market where this renewable energy would be much more abundant and the price curve would shift to a lower price, as well as a significant reduction in greenhouse emissions.