Assessing the benefits of capacity payment, feed-in-tariff and time-of-use programme on long-term renewable energy sources integration

Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration

A system dynamics approach to study the long-term interaction of the natural gas market and electricity market comprising high penetration of renewable energy resources

Due to the gas consumption of some power plants for electricity generation and providing an acceptable level of flexibility, the interaction of natural gas markets and electricity markets is inevitable. One of the main challenges of policymakers in the energy sector coupling is the investigation of such interactions. Our main goal is to analyze the effect of the penetration of renewable energy resources on the behavior of gas markets and vice versa from the policymaker’s viewpoint. Moreover, we tend to study the effect of an external shock on the behavior of the whole system and the role of renewable resources in mitigating these side effects. Therefore, we used System Dynamic Approach to model the long-term behavior of the natural gas markets to extend the existed models of the electricity markets behavior and couple these markets. The Net Present Value method was used for the economic assessment of the investment in the development of gas reserves, and new stock and flow variables were defined to simulate this development. The simulations are performed for four scenarios by using a valid case study. Considering the results of simulations and sensitivity analysis, as the wind capacity incentive rose, the gas and electricity prices declined and their fluctuation increased during the time horizon. Although the effect of the gas market shock on the system depends on the time of occurrence, as the penetration of renewable units increased, the severity of its side effects decreased and the price jumps in the markets were mitigated.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi-directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q-learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation-based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.© 2020 The Institution of Engineering and Technology. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)fi=vertaisarvioitu|en=peerReviewed

Capacity withholding assessment of power systems considering coordinated strategies of virtual power plants and generation companies

This paper presents a multi-level optimization framework for power system operators' joint electricity markets capacity-withholding assessment. The main contribution of this research is that three capacity-withholding indices are introduced for day-ahead, intra-day, and real-time scheduling of the system that detect the capacity withholding and arbitrage opportunities of Virtual Power Plants (VPPs) and non-utility fossil-fueled GENeration COmpanies (GENCOs) in an ex-ante procedure. A three-level optimization process is used so that the system operator can estimate the coordinated bidding of VPPs/GENCOs in different energy and ancillary services markets to prevent the formation of withholding groups. The first level problem consists of two stages. The first stage estimates the optimal capacity withholding and arbitrage bidding strategy of VPPs/GENCOs, and the second stage determines the optimal system scheduling for the day-ahead horizon. A full competition algorithm is also carried out to evaluate the competition states of VPPs/GENCOs. The second and third level problems consist of two optimization stages for the intra-day and real-time optimization horizons. At the first stage of each level, the process estimates the coordinated bidding of VPPs/GENCOs, and at the second stage of each level, the system resources are optimally dispatched. The proposed method is applied to 30-bus and 118-bus IEEE test systems. The proposed algorithm reduced the maximum locational marginal prices of 30-bus and 118-bus test systems by about 57.04% and 44.73% compared to the normal and the worst-case contingency operating conditions, respectively. Furthermore, the proposed method reduced the average values of day-ahead, intra-day and real-time dynamic capacity withholding indices of the 118-bus test system by about 32.92%, 40.1%, and 46.85%, respectively.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Smart household management systems with renewable generation to increase the operation profit of a microgrid

During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.fi=vertaisarvioitu|en=peerReviewed

Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets

This paper presents an integrated framework for the optimal resilient scheduling of an active distribution system in the day-ahead and real-time markets considering aggregators, parking lots, distributed energy resources, and Plug-in Hybrid Electric Vehicles (PHEVs) interactions. The main contribution of this paper is that the impacts of traffic patterns on the available dispatchable active power of PHEVs in day-ahead and real-time markets are explored. A two stage framework is considered. Each stage consists of a four-level optimization procedure that optimizes the scheduling problems of PHEVs, parking lots and distributed energy resources, aggregators, and active distribution system. The distribution system procures ramp-up and ramp-down services for the upward electricity market in a real-time horizon. The active distribution system can utilize a switching procedure to sectionalize its system into a multi-microgrid system to mitigate the impacts of external shocks. The model was assessed by the 123-bus test system. The proposed algorithm reduced the interruption and operating costs of the 123-bus test system by about 94.56% for the worst-case external shock. Further, the traffic pattern decreased the available ramp-up and ramp-down of parking lots by about 58.61% concerning the no-traffic case.© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Bi-Level Operation Scheduling of Distribution Systems with Multi-Microgrids Considering Uncertainties

A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO’s point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem. The effectiveness of the proposed model is demonstrated on a real-test MMG system

Coordinated flexible energy and self-healing management according to the multi-agent system-based restoration scheme in active distribution network

This study presents the optimal model of the coordinated flexible energy and self-healing management (C-FE&SH-M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self-healing method (SHM) is related to multi-agent system-based restoration process (MAS-based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33-bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.© 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.M. Shafie-khah acknowledges FLEXIMAR-project (Novel marketplace for energy flexibility), which has received funding from Business Finland Smart Energy Program, 2017-2021. J.P.S. Catalão acknowledges the support by FEDER through COMPETE 2020 and by FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017).fi=vertaisarvioitu|en=peerReviewed

Distributed energy resource and network expansion planning of a CCHP based active microgrid considering demand response programs

This paper addresses the network expansion planning of an active microgrid that utilizes Distributed Energy Resources (DERs). The microgrid uses Combined Cooling, Heating and Power (CCHP) systems with their heating and cooling network. The proposed method uses a bi-level iterative optimization algorithm for optimal expansion and operational planning of the microgrid that consists of different zones, and each zone can transact electricity with the upward utility. The transaction of electricity with the upward utility can be performed based on demand response programs that consist of the time-of-use program and/or direct load control. DERs are CHPs, small wind turbines, photovoltaic systems, electric and cooling storage, gas fired boilers and absorption and compression chillers are used to supply different zones’ electrical, heating, and cooling loads. The proposed model minimizes the system’s investment, operation, interruption and environmental costs; meanwhile, it maximizes electricity export revenues and the reliability of the system. The proposed method is applied to a real building complex and five different scenarios are considered to evaluate the impact of different energy supply configurations and operational paradigm on the investment and operational costs. The effectiveness of the introduced algorithm has been assessed. The implementation of the proposed algorithm reduces the aggregated investment and operational costs of the test system in about 54.7% with respect to the custom expansion planning method.fi=vertaisarvioitu|en=peerReviewed

Optimal operation of hybrid AC/DC microgrids under uncertainty of renewable energy resources : A comprehensive review

The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy resources are reviewed.fi=vertaisarvioitu|en=peerReviewed