Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach

Cai, Wei; Mohammaditab, Rasoul; Fathi, Gholamreza; Wakil, Karzan; Ebadi, Abdol Ghaffar; Ghadimi, Noradin

doi:10.1016/j.renene.2019.05.008

Cited by 288 publications

(60 citation statements)

References 21 publications

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“…In [24], a multi-energy microgrid operation incorporated with high penetration of RES was optimized via a hybrid stochastic/interval framework exposed by multi-energy demands and RES power output variation. An optimal bidding strategy of compressed air energy storage system with the aim of profit maximization under a hybrid robust/stochastic approach was developed by [25]. The market price uncertainty was modeled by a set of scenarios, while the maximum capacity of CAES cavern is handled by a robust strategy.…”

Section: B Literature Reviewmentioning

confidence: 99%

“… In [14][15][16][17][18][19][20], although the authors have investigated the mobility of battery-based energy storage into networkconstrained unit commitment, they have not extensively focused on environmental issues, the flexibility of demand-side resources, and marketclearing process.  In [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], the authors mainly have utilized deterministic, stochastic and robust-based optimization approaches to solve the problem, while the operator at times preferred to differentiate between the risk levels of the existing uncertainties and manage them depending on the different optimization techniques  In [21][22][23][24][25][26], the authors have not applied a hybrid optimization approach in the market-clearing process, while these kinds of techniques can provide major benefits for the market operator to handle uncertainties in real-time dispatch. Hence, this paper applies a new two-stage robuststochastic framework into energy market-clearing constrained to the power grid, environmental issues, and rail transport network (RTN) to achieve high-efficiency scheduling of ESS and handle the uncertainties associated with demand and wind power generation.…”

Section: Contributionsmentioning

confidence: 99%

“…,0 , tr tr T EE  (23) The constraints of PSS demand in the first stage are described by (24)- (30). The relationship between demand blocks and the total load considering adjustable demand are given by (24) and (25). In addition, the limit on the demand block is mentioned by (26).…”

Section: ) First Stage Constraintsmentioning

confidence: 99%

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Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

et al. 2020

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This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in realtime. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

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Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

et al. 2020

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“…The PSO algorithm was first introduced by Kennedy and Eberhart, [32][33][34] which is inspired from the conduct of bird swarms via a humble concept of social collaboration, and it was quickly and effectively utilized in the context of the population-based optimization methods. 32,33,[35][36][37][38][39][40][41][42] In a problem with N particles, in which a particle is a probable answer of a practical problem in a seeking area with D dimension, velocity/location of particle i in the jth dimension at the tth step time is updated using the PSO method by…”

Section: Basic Concepts Of Pso and Suggested Adaptive Pso Schemementioning

confidence: 99%

“…The PSO algorithm was first introduced by Kennedy and Eberhart, which is inspired from the conduct of bird swarms via a humble concept of social collaboration, and it was quickly and effectively utilized in the context of the population‐based optimization methods …”

Section: Proposed Approachmentioning

confidence: 99%

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Zeng

Berti

2019

Computational Intelligence

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Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time‐of‐use, and real‐time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use‐DR and real‐time pricing‐DR problems. The proposed model could reduce the cost about 10%.

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A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

Wang

Wang²,

Zhang³

et al. 2021

Int Trans Electr Energ Syst

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Summary The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh.

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Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach

Cited by 288 publications

References 21 publications

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

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