Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Hassan, Muhammad Wajahat; Rasheed, Muhammad Babar; Javaid, Nadeem; Nazar, Waseem; Akmal, M.

doi:10.3390/en11102833

Cited by 6 publications

(8 citation statements)

References 60 publications

(62 reference statements)

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“…The recent literature has provided enough directions to make the system more reliable, we have combined all of the major concepts proposed individually in the cited papers i.e, load forecasting to dispatch of generation along with reserve using a heuristic algorithm, incorporation of uncertain RES to the provision of a spinning reserve through BSS. Furthermore, the discussion and analysis in this paper extend the preliminary results in our earlier paper [31] in various aspects. First, here we have implemented the load forecasting technique instead of taking arbitrary load demand.…”

Section: Motivation and Background Literaturesupporting

confidence: 84%

“…Therefore, in this regard some studies have quantified the provision of reserve energy as given in [25]- [30]. These studies have focused solely on optimizing the economic operation of the combined dispatch of energy and reserve [31].…”

Section: Motivation and Background Literaturementioning

confidence: 99%

“…The a part of predicted load demand is fulfilled by RES and LFTGUs are economically dispatched for the remaining part of load demand. Therefore, PSO is used to get optimized results of the dispatch of LFTGUs, providing the IEEE standard data set of generators already used in our previous journal paper [31]. For instance, where LFTGUs, being dispatched within their operational limits, are not able to fulfill the total load demand, the BSS is involved in supplying the power.…”

Section: Optimization Modelmentioning

confidence: 99%

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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

et al. 2021

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In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end. INDEX TERMSCo-Dispatch, Spinning reserve, State of charge, Optimization, Renewable energy resources, Battery storage system, Particle swarm optimization NOMENCLATURE The main notations used in this paper are stated below. Additional symbols are defined in the article where required.

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Section: Motivation and Background Literaturesupporting

confidence: 84%

Section: Motivation and Background Literaturementioning

confidence: 99%

Section: Optimization Modelmentioning

confidence: 99%

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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

et al. 2021

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“…The study did not, however, use a grid model where the power flow is a function of voltage angle difference between the two nodes. Another study [24] presented a joint energy and reserve model that did not include energy storage systems (ESS) and demand response (DR) as well as aggregated all technologies in one node. Joint energy and reserve model was presented in [25] where authors observed the influence of electric vehicle (EV) fleet on the system operation.…”

Section: Introductionmentioning

confidence: 99%

The role of the energy storage and the demand response in the robust reserve and network-constrained joint electricity and reserve market

Mimica¹,

Sinovčić²,

Jokic

et al. 2022

Electric Power Systems Research

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“…Reference [2][3][4][5][6] establishes an optimization model aiming at maximizing the profit of the system. Reference [7][8][9][10][11] synthetically considers the operation cost and emission cost of microgrid, and establishes a multi-objective optimization model to minimize the economic cost and emission of microgrid. Reference [12] analyzed the characteristics of distributed generation and the mathematical model of optimization objectives in detail, and proposed a multi-objective immune algorithm of niche evolution for the optimal management of distributed power output in microgrid.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Economic Dispatch Model of Microgrid Containing Energy Storage Components Based on a Variant of NSGA-II Algorithm

2019

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With the development of microgrid, in order to improve the economy of the microgrid and intelligent service of electric power marketing, the proper management of the output of micro-source in microgrid and power exchange between grids is an urgent problem to be solved. Considering the interests of multiple stakeholders, such as users, power grids, renewable energy and battery, a dynamic economic dispatch model of microgrid is proposed in this paper based on time-of-use power price mechanism. Using a variant of Non-Dominated Sorting Genetic Algorithm (NSGA)-II algorithm, at the same time, an external penalty function is introduced to deal with the constraint conditions, which is convenient for solving multi-objective optimization models. Based on the data of load forecasting and renewable energy output in microgrid, the function of battery and time-of-use power price mechanism is considered to optimize the output of controllable micro-source in the system, in order to achieve the optimization of microgrid dispatch. The model established in this paper considers the overall economic optimization of multi-objective and multi-interest groups within the microgrid, and hence, can get a more comprehensive and reasonable scheduling scheme. It provides effective help for the operation of micro grid system, and realizes the electric power marketing for demand side, so as to provide help for improving the power marketing’s economy and intelligent service.

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Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Cited by 6 publications

References 60 publications

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

The role of the energy storage and the demand response in the robust reserve and network-constrained joint electricity and reserve market

Dynamic Economic Dispatch Model of Microgrid Containing Energy Storage Components Based on a Variant of NSGA-II Algorithm

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