Electricity Market Structure with Individual Pricing Mechanism

Mnatsakanyan, Ashot; AlMazrooqi, Ali Hussain; Muruaga, Endika Bilbao; Banda, P.

doi:10.1109/pedg.2018.8447757

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In the context of decentralized energy management, it is crucial to consider the market dynamics. Mnatsakanyan et al [5] propose an innovative electricity market structure emphasizing individual pricing mechanisms for fairer demand response benefits distribution. Brahmia et al [6] highlight the challenges of electricity price forecasting in multi-microgrid systems, advocating for advanced predictive models.…”

Section: Introductionmentioning

confidence: 99%

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

Olivieri,

Volpe,

Mangini

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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This paper introduces an innovative approach to designing a user-based Heating, Ventilation, and Air-Conditioning (HVAC) system management connected with the District Energy Management System. By classifying the users into dynamic energy consumption classes to reward energy efficiency and penalize excessive use, users can modify their behavior to pass to a less expensive and more virtuous consumption class. To this aim, a blockchain platform determines the rewards and penalties and, by a K-means clustering algorithm, categorizes users into respective groups. Then, a Class Follower Problem is formulated and solved by a Model Predictive Control (MPC) strategy integrated with a Long Short-Term Memory network as a predictive model. If the users follow the suggestions proposed by the controller, i.e., the thermostat set-points and the time intervals in which the HVAC system must be switched off or on, the users can be located in a more virtuous consumption class. A case study conducted within an energy district in Bari (Italy) shows how the proposed architectural framework tuned thermal regulation in intelligent buildings while concurrently achieving energy optimization.Note to Practitioners-This paper addresses the challenge of efficiently managing HVAC systems in smart districts through a novel blockchain-based framework and an optimization strategy solved by an MPC approach. The objective is to incentivize users to optimize their energy consumption by introducing dynamic Consumption Classes that reward energy efficiency and penalize inefficient utilization. For practitioners, this strategy translates to a granular level of energy management that not only adapts to individual behaviors but also aligns with broader sustainability goals. Integrating the blockchain platform ensures a transparent and secure method for managing and recording energy usage. At the same time, adopting MPC with Long Short-Term Memory Networks offers accurate forecasts and adjustments to enhance system responsiveness. Although the study focuses on HVAC systems, the principles may be extended to other energy-intensive applications, providing a comprehensive tool for energy management and user engagement in smart cities. Future research could integrate renewable energy sources and explore the implications of user-driven adjustments on the overall energy distribution and efficiency.

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Section: Introductionmentioning

confidence: 99%

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

Olivieri,

Volpe,

Mangini

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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“…However, the yearly electricity load pattern in Sharjah changes based on temperature variations, seasonal effects, peak/off-peak hours in a day etc. The electricity supplied to a city must be generated as per the demand in the city [4], [5]. Both over production and under production of electricity would cause financial loss to the operators [6].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Regression for Electricity Load Forecasting in Power Systems

Joy,

Bansal,

Ghenai

et al. 2024

Preprint

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The development of smart grids in power system necessitates the need for forecasting the electricity load for the safe and economic functioning of electricity markets. A case study has been carried out considering a city's electricity load data using Multivariate Regression model. An input database of the model is generated taking into account of peak and off-peak hours based on maximum and minimum load data obtained from the utility operator. The characteristics of the electricity load over the whole year have been primarily analyzed to obtain a better intuition on the load behavior. In this context, the information in the form of temperature, days, different time duration i.e., peak and off-peak hours and past load data have been given as input to the regression model. The accuracy of the method has been evaluated using Root Mean Square Error (RMSE). The results of the adapted model have been compared with Neural Network, Ensemble and Kernel methods.

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“…From a technical perspective, electricity markets have been studied for distributed generation [19,20], where the topic is addressed, taking no consideration of the environment or a transition from conventional generation to distributed generation. There is also the case of [21,22] in which demand response is included for the establishment of an electricity market but is only interested in how to charge prices to the consumer and not answering the question of the assignment of energy resources.…”

Section: Introductionmentioning

confidence: 99%

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Garcia,

Águila,

Ortiz

et al. 2024

Energies

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This research incorporates an electricity market model based on a stochastic allocation of distributed resources and the analysis of an optimal demand response for a smart microgrid. This research develops a methodology that allows the application and comparison of various demand-response mechanisms and the analysis of the differences between them and the case of no-demand response, emphasizing economics, environmental care, demand curves, and other factors. By enabling more active participation by residential users of the smart microgrid, these demand-response methods help to flatten the demand curve and support the goals set by the electricity market model. Both conventional and non-conventional generators compete in the electricity market, with renewable energy sources preferred to encourage green generation. Conventional generators are required to supply electricity gradually, starting with the lowest pollution level. In addition, conventional generators are compensated for dispatch, system reliability, and availability. In addition, random variables are used in this study to predict initial load, solar radiation analysis, and biomass input before resources are optimized to meet demand.

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Electricity Market Structure with Individual Pricing Mechanism

Cited by 4 publications

References 18 publications

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

Multivariate Regression for Electricity Load Forecasting in Power Systems

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

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