Dynamic optimal power flow of combined heat and power system with Valve-point effect using Krill Herd algorithm

Adhvaryyu, Pradosh Kumar; Chattopadhyay, Pabitra; Bhattacharya, A.

doi:10.1016/j.energy.2017.03.046

Cited by 30 publications

(22 citation statements)

References 25 publications

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“…The operation constraints of the WPP and the PV should satisfy the maximum output constraints, namely, the outputs of the WPP and the PV should not be greater than the system's allowable output, which has already been studied in Reference [21]. The constraints of the TPP operation comprise the maximum/minimum output constraints, the upwards/downwards climbing constraints, and start-shutdown time constraints, which have already been studied in Reference [28].…”

Section: (5) Other Power Source Constraintsmentioning

confidence: 99%

“…The second method is to weight multi-objective functions into an integrated single-objective function. In general, the weighted coefficients can be obtained subjectively or via weight determination methods for different objective functions [28]. In a previous study, the weight coefficients were mainly calculated by using the entropy weight method [29].…”

Section: Multi-objective Model Solutionmentioning

confidence: 99%

“…The payoff table mainly reflects the relationships between different objective functions [28]. The values of the objective functions were calculated when the MECS operated under other optimization objectives.…”

Section: Payoff Tablementioning

confidence: 99%

“…The improved IEEE 14-bus system with four thermal power units was used as the simulation system [28]. A WPP, a PV, an HS, and ESDs were connected with the simulation system to analyze the complementarity of the different power sources.…”

Section: Basic Datamentioning

confidence: 99%

“…The proposed model involved a nonlinear objective function and constraints belonging to the class of mixed integer nonlinear programming problems. As GAMS is better suited to mixed integer programming problems, the objective function and the constraints were linearized [28]. Figure 7 shows the optimization flowchart for the MECS scheduling model.…”

Section: Basic Datamentioning

confidence: 99%

See 4 more Smart Citations

A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies

Li²,

Tan

et al. 2018

Applied Sciences

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In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into a multi-energy complementary system (MECS). Firstly, a scenario-generating technique is proposed for uncertainty factors using the Wasserstein method and the improved K-medoids theory. Then, a multi-objective model and solution algorithm are constructed under the objectives of attaining the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations. Meanwhile, the influence of different ESD operation modes on MECS operation is discussed, specifically, the longest life cycle (LLC) and the optimum economic efficiency (OEE). Thirdly, in order to solve the multi-objective model, a solution algorithm is proposed by using the rough set method to convert the multi-objective model into a single objective model based on the payoff table. Moreover, the complementary features of the MECS are evaluated in terms of the load tracking degree, HS secondary peaking capacity, and units of coal consumption. Finally, the improved IEEE 14-bus system is chosen for the simulation analysis. The results show that (1) the proposed uncertainty simulation method can efficiently generate the most representative scenarios; (2) MECSs can utilize complementary power sources, the OEE mode can better optimize MECS scheduling, and the LLC mode can ensure the ESDs’ life cycles; (3) the scheduling scheme of MECS operation reach the optimal level when the capacity ratio of ESD:WPP–PV iso [0.62, 0.77] in the OEE mode and [1, 1.08] in the LLC mode on a typical summer day, and the ratio is [0.92, 1] in the OEE mode and [1.23, 1.31] in the LLC mode on a typical winter day. Therefore, the proposed model provides effective decision-making support for designing the optimal plan for MECS operation.

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Section: (5) Other Power Source Constraintsmentioning

confidence: 99%

Section: Multi-objective Model Solutionmentioning

confidence: 99%

Section: Payoff Tablementioning

confidence: 99%

Section: Basic Datamentioning

confidence: 99%

Section: Basic Datamentioning

confidence: 99%

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A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies

Li²,

Tan

et al. 2018

Applied Sciences

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A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies

Javadi¹,

Nezhad²,

Razavi³

et al. 2020

Evolutionary Computation in Scheduling

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Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

Hassan

Elsayed

Kamel

et al. 2022

Intl J of Energy Research

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Summary During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs.

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Dynamic optimal power flow of combined heat and power system with Valve-point effect using Krill Herd algorithm

Cited by 30 publications

References 25 publications

A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies

A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies

A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies

Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

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