Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Environmental aspects have been highlighted in architecting future energy systems where sustainable development plays a key role. Sustainable development in the energy sector has been defined as a potential solution for enhancing the energy system to meet the future energy requirements without interfering with the environment and energy provision. In this regard, studying the cross-impact of various energy vectors and releasing their inherent operational flexibility is main topic. Thecoordinationofvariousenergyvectorsundertheconceptofmulti-energysystem (MES)hasintroducednewsourcesofoperationalflexibilitytothesystemmanagers. MES considers both interactions among the energy carriers and the decision makers in an interdependent environment to increase the total efficiency of the system and reveal the hidden synergy among energy carriers. This thesis addresses a framework for modeling multi-energy players (MEP) that are coupled based on price signal in multi-energy system (MES) in a competitive environment. MEP is defined as an energy player who can consume or deliver more than one type of energy carriers. At first, the course of evolution for the energy system from today independent energy systems to a fully integrated MES is presented and the fractal structure is described for of MES architecture. Moreover, the operational behavior of plug-in electric vehicles’ parking lots and multi-energy demands’ external dependency are modeled in MES framework to enhance the operational flexibility of local energy systems (LES). In the fractal environment, there exist conflicts among MEPs’ decision making in a same layer and other layers. Realizing the inherent flexibility of MES is the main key for modeling the conflicts in this multi-layer structure. The conflict between two layers of players is modeled based on a bi-level approach. In this problem, the first level is the MEP level where the player maximizes its profit while satisfying LES energy exchange. The LES’s exchange energy price is the output of this level. In the lower level, the LESs schedule their energy balance, based on the upper level input price signal. The problem is transformed into a mathematical program with equilibrium constraint (MPEC) through duality theory. In the next step, high penetration of multi-energy players in the electricity market is modeled and their impacts on electricity market equilibrium are investigated. In such a model, MEP participates in the local energy and wholesale electricity markets simultaneously. MEP and the other players’ objectives in these two markets conflict with each other. Each of these conflicts is modeled based on bi-level programming. The bi-level problems are transformed into a single level mixed-integer linear problem by applying duality theory

Sustainable distribution network planning considering multi-energy systems and plug-in electric vehicles parking lots

Entre todos os recursos associados à evolução das redes elétricas para o conceito de smart grid, os sistemas de multi-energia e os veículos eléctricos do tipo plug-in (PEV) são dois dos principais tópicos de investigação hoje em dia. Embora estes recursos possam acarretar uma maior incerteza para o sistema de energia, as suas capacidades de demanda/armazenamento flexível de energia podem melhorar a operacionalidade do sistema como um todo. Quando o conceito de sistemas de multi-energia e os parques de estacionamento com estações de carregamento para os PEVs são combinados no sistema de distribuição, a demanda pode variar significativamente. Sendo a demanda de energia uma importante informação no processo de planeamento, é essencial estimar de precisa essa demanda. Deste modo, três níveis padrão de carga podem ser extraídos tendo em conta a substituição da procura entre carriers de energia, a demanda associada ao carregamento dos PEVs, e presença de parques de estacionamento com estações de carregamento no sistema. A presença de PEVs num sistema multi-energia obriga a outros requisitos (por exemplo, um sistema de alimentação) que devem ser fornecidos pelo sistema, incluindo as estações de carregamento. A componente elétrica dos PEVs dificulta a tarefa ao operador do sistema na tentativa de encontrar a melhor solução para fornecer os serviços necessários e utilizar o potencial dos PEVs num sistema multi-energia. Contudo, o comportamento sociotécnico dos utilizadores de PEVs torna difícil ao operador do sistema a potencial gestão das fontes de energia associada às baterias. Desta forma, este estudo visa providenciar uma solução para os novos problemas que irão ocorrer no planeamento do sistema. Nesta tese, vários aspetos da integração de PEVs num sistema multi-energia são estudados. Primeiro, um programa de resposta à demanda é proposto para o sistema multi-energia com tecnologias do lado da procura que possibilitem alternar entre fornecedores de serviços. Em seguida, é realizado um estudo abrangente sobre as questões relativas à modelação dos PEVs no sistema, incluindo a modelação das incertezas, as preferências dos proprietários dos veículos, o nível de carregamento dos PEV e a sua interação com a rede. Posteriormente é proposta a melhor estratégia para a participação no mercado de energia e reserva. A alocação na rede e os possíveis efeitos subjacentes são também estudados nesta tese, incluindo o modelo dos PEVs e dos parques de estacionamento com estações de carregamento nesse sistema de multi-energia.Among all resources introduced by the evolution of smart grid, multi-energy systems and plugin electric vehicles are the two main challenges in research topics. Although, these resources bring new levels of uncertainties to the system, their capabilities as flexible demand or stochastic generation can enhance the operability of system. When the concept of multienergy systems and plug-in electric vehicles (PEV) parking lots are merged in a distribution system, the demand estimation may vary significantly. As the main feed of planning process, it is critical to estimate the most accurate amount of required demand. Therefore, three stages of load pattern should be extracted taking into account the demand substitution between energy carriers, demand affected by home-charging PEVs, and parking lot presence in system. The presence of PEVs in a multi-energy system oblige other requirements (i.e. fueling system) that should be provided in the system, including charging stations. However, the electric base of PEVs adds to the responsibilities of the system operator to think about the best solution to provide the required services for PEVs and utilize their potentials in a multi-energy concept. However, the socio-technical behavior of PEV users makes it difficult for the system operator to be able to manage the potential sources of PEV batteries. As a result, this study tries to raise the solution to new problems that will occur for the system planners and operators by the future components of the system. In this thesis, various aspects of integrating PEVs in a multi-energy system is studied.Firstly, a carrier-based demand response program is proposed for the multi-energy system with the technologies on the demand side to switch between the carriers for providing their services. Then, a comprehensive study on the issues regarding the modeling of the PEVs in the system are conducted including modeling their uncertain traffic behavior, modeling the preferences of vehicle owners on the required charging, modeling the PEV parking lot behavior and its interactions with the network. After that the best strategy and framework for participating the PEVs energy in the energy and reserve market is proposed. The allocation of the parking lot in the network and the possible effects it will have on the network constraints is studied. Finally, the derived model of the PEVs and the parking lot is added to the multi-energy system model with multi-energy demand

A Mixed Epistemic-Aleatory Stochastic Framework for the Optimal Operation of Hybrid Fuel Stations

The fast development of technologies in the smart grids provides new opportunities such as co-optimization of multi-energy systems. One of the new concepts that can utilize multiple energy sources is a hybrid fuel station (HFS). For instance, an HFS can benefit from energy hubs, renewable energies, and natural gas sources to supply electric vehicles along with natural gas vehicles. However, the optimal operation of an HFS deals with uncertainties from different sources that do not have similar natures. Some may lack in term of historical data, and some may have very random and unpredictable behavior. In this study, we present a stochastic mathematical framework to address both types of these uncertainties according to the innate nature of each uncertain variable, namely: epistemic uncertainty variables (EUVs) and aleatory uncertainty variables (AUVs). Also, the imprecise probability approach is introduced for EUVs utilizing the copula theory in the process, and a scenario-based approach combining Monte Carlo simulation with Latin Hypercube sampling is applied for AUVs. The proposed framework is employed to address the daily operation of a novel HFS, leading to a two-stage mixed-integer linear programming problem. The proposed approach and its applicability are verified using various numerical simulations

Dispatch of vehicle-to-grid battery storage using an analytic hierarchy process

The number of electric vehicles (EVs) is expected to increase significantly in the future to combat air pollution and reduce reliance on fossil fuels. This will impact on the power system. However, appropriate charging and discharging of EVs through vehicle-to-grid operations could also provide support for the power system and benefits for the EV owners. This raises the questions of when and how EV battery storage should be dispatched, taking into account both vehicle users’ and power system’s requirements and priorities, as well as the constraints of the battery system. This paper proposes a novel decentralized dispatch strategy based on the Analytic Hierarchy Process (AHP) taking into account the relative importance of the different criteria including cost, battery state of charge, power system contingency and load levelling. The proposed AHP-based dispatch strategy was tested on an IEEE Reliability Test System with different EV numbers and capacities to investigate the efficacy of such an approach. The simulation results demonstrate the feasibility and benefits of this dispatch strategy

Linearized Stochastic Optimization Framework for Day-Ahead Scheduling of a Biogas-Based Energy Hub Under Uncertainty

Energy hubs (EHs), due to their multiple nature in the production, consumption, and storage of energy, as well as the ability to participate in different energy markets, have made their optimal and profitable scheduling important for operators. Considering the literature review, one of the main motivations of this paper is the use of biogas as a pivotal fuel and through production using biomass in the structure of EHs. Therefore, this paper proposes a linearized optimization framework for optimal scheduling of a biogas-based EH for participation in day-ahead (DA) electricity and thermal energy markets. The proposed EH directly converts local biomass into biogas, thereby providing the fuel to generate electricity and thermal. This EH comprises digester, biogas storage, electric heat pump (EHP), biogas burner CHP and boiler, solar farm, electrical storage, and internal electrical and thermal loads. In this framework, the uncertainties related to solar radiation and the DA price are modeled to generate random scenarios using the Monte-Carlo method. The proposed EH is simulated for numerical studies based on data from Finland’s two selected spring and autumn days. The results show the optimal performance of the EH because it can participate in the electricity and thermal markets by using the biogas produced inside it and providing complete internal loads, and earns a decent income. In the autumn, operating the EH is more economical than in the spring. Moreover, comparative results have shown that eliminating the biogas unit and using natural gas significantly increases the expected costs of EH.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

A Real-time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC