Design of an Auction-based Local Energy Market for Integrated Electricity and Heat Networks Coordinated with Wholesale Market

This article presents a market-based framework for coupling of electricity and heat sectors at the local level via power-to-heat (P2H) units. The considered local energy market (LEM) is designed based on an auction-based energy trading process which is settled by the integrated energy system operator (IESO) with the objective of social welfare maximization. Moreover, as part of the suggested mechanism, the coordination between the IESO and the transmission system operator (TSO) is considered to evaluate the mutual impact of the designed LEM on the wholesale electricity market (WEM) and vice versa. To this end, a bi-level programming model is employed, in which the LEM clearing problem is implemented at its upper level (UL) while the WEM clearing problem is executed at its lower level (LL). To assess the operation of the LEM and its coordination with the WEM, a case study is considered in which an integrated energy system (IES), including a 13-node electric distribution system and a 4-node district heating system, is connected to a 6-node transmission system.© IET. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.fi=vertaisarvioitu|en=peerReviewed

Multi-energy Microgrids Incorporating EV Integration : Optimal Design and Resilient Operation

There are numerous opportunities and challenges in integrating multiple energy sources, for example, electrical, heat, and electrified transportation. The operation of multi-energy sources needs to be coordinated and optimized to achieve maximum benefits and reliability. To address the electrical, thermal, and transportation electrification energy demands in a sustainable and environmentally friendly multi-energy microgrid, this paper presents a mixed integer linear optimization model that determines an optimized blend of energy sources (battery, combined heat and power units, thermal energy storage, gas boiler, and photovoltaic generators), size, and associated dispatch. The proposed energy management system seeks to minimize total annual expenses while simultaneously boosting system resilience during extended grid outages, based on an hourly electrical and thermal load profile. This approach has been tested in a hospital equipped with an EV charging station in Okinawa, Japan through several case studies. Following a M1/M2/c queuing model, the proposed grid-tied microgrid successfully integrates EVs into the system and assures continued and economic power supply even during grid failures in different weather conditions.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Optimal planning of a virtual power plant hosting an EV parking lot

With the increasing penetration of electric vehicles (EV) in the future, VPPs can take some actions for meeting their demand. This way, VPPs can increase their income by selling electric power to EVs and utilizing the battery of EVs as energy storage to facilitate the deployment of renewable energy resources. However, investing too much in charging stations may not have an acceptable return on investment. In this paper, we study the optimal operation and planning of a VPP which is located to certain part of the network and is composed of wind turbines, PV units, as well as unidirectional and bidirectional EV charging stations. In our proposed approach, optimal planning is done considering that the system will be operated optimally. According to the simulation results, EV owners' behavior could have a significant impact on the optimal planning decision of the VPP. In addition, optimal number of the unidirectional and bidirectional EV charging stations depend on the share of the PV and wind generation and the capacity of the line between the VPP and upstream grid.©2022 IET. This paper is a postprint of a paper submitted to and accepted for publication in CIRED Porto Workshop 2022: E-mobility and power distribution systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Risk-Based Decision Support Model for the Optimal Operation of a Smart Energy Distribution Company for Enabling Emerging Resources

In this paper, a risk-based decision support model is developed for a smart energy distribution company, enabling emerging resources like renewable energy sources, electric vehicles and demand response programs in a holistic approach. Because of the inherent uncertainties of these emerging resources, the conditional value-at-risk (CVaR) method is adopted to restrict the distribution company’s risk. A risk aversion parameter sensitivity analysis is also provided on the optimal operation of the smart energy distribution company. The proposed model is thoroughly tested on a 15-bus distribution grid system, and the numerical results prove the effectiveness of the model in risk management

The Simultaneous Impacts of Seasonal Weather and Solar Conditions on PV Panels Electrical Characteristics

Solar energy usage is thriving day by day. These solar panels are installed to absorb solar energy and produce electrical energy. As a result, the efficiency of solar panels depends on different environmental factors, namely, air temperature, dust (aerosols and accumulated dust), and solar incidence, and photovoltaic panel angles. The effects of real conditions factors on power and efficiency of photovoltaic panels are studied in this paper through testing the panel in real environmental tests. To study the mentioned parameters precisely, two panels with different angles are used. The case study is regarding a region of Tehran, Iran, in summer and winter seasons. The results show that panel efficiency during winter is higher than summer due to air temperature decrement. It is discovered that among air pollutants, Al and Fe have the most share in polluting the air that affect the photovoltaic efficiency. Moreover, measuring the accumulated dust on the panels shows more amount in winter in comparison with summer. The important point in studying the effect of tilt angle is that inconformity between solar incidence and photovoltaic panel angles would result in solar radiation absorption and eventually panel efficiency loss and also, photovoltaic panel installation angle would affect the amount of dust deposited on its surface.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Techno-economic assessment of energy storage systems in multi-energy microgrids utilizing decomposition methodology

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations

Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage

© 2010-2012 IEEE. Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems' operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach

Multi-Agent Architecture for Peer-to-Peer Electricity Trading based on Blockchain Technology

The security of smart grids is put at risk due to their automation and remote access features. Blockchain technology can be used as a distributed ledger where data is stored and all the data transactions between the different entities of a smart grid are signed to protect them from such attacks. This paper proposes a multi-agent system (MAS) that combines smart contracts and blockchain to enable Peer-to-Peer electricity trading in a MicroGrid (MG) scenario, without the need for human intervention. The use of blockchain technology helps reduce transaction costs and allows to make micro transactions in the proposed market. Blockchain also improves the security of the platform because all the involved actors can be certain about the authorship of the information produced in the system. Finally, the use of a MAS and the possibility of negotiating between the agents helps obtain an optimal state in the system in which the costs of energy are minimal and the local production of energy is profitable.©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Planning of Smart Microgrids with High Renewable Penetration Considering Electricity Market Conditions

In this paper, a new method for optimal sizing of distributed generation (DG) is presented in order to minimize electricity costs in smart microgrids (MGs). This paper presents a study of the effect of wholesale electricity market on smart MGs. The study was performed for the Ekbatan residential complex which includes three smart MGs considering high penetration of renewable energy resources and a 63/20 kV substation in Tehran, Iran. The role of these smart MGs in the pool electricity market is a price maker, and a game-theoretical (GT) model is applied for their bidding strategies. The objective cost function considers different cost parameters in smart MGs, which are optimized using particle swarm optimization (PSO). The results show that applying this method is effective for economic sizing of DGs.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed