Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system

Jiang, Yibo; Xu, Jian; Sun, Yang; Wei, Congying; Wang, Jing; Ke, Deping; Xiong, Lei; Yang, Jun; Peng, Xiaotao; Tang, Bowen

doi:10.1016/j.apenergy.2017.01.030

Cited by 109 publications

(43 citation statements)

References 30 publications

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“…Jiang et al [20] proposed a management model for a residential HES provided by wind generation, micro-combined heat and power generation and smart appliances, enrolled in a real-time pricing (RTP) program. Additionally, optimal behavior of several aggregated HESs is analyzed by means of a day-ahead stochastic economic dispatch (ED) and UC model based on MILP.…”

Section: Literature Reviewmentioning

confidence: 99%

Day-Ahead Optimal Battery Operation in Islanded Hybrid Energy Systems and Its Impact on Greenhouse Gas Emissions

et al. 2019

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This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm.Incorporation of ESS can improve the accommodation of renewable generation while reducing greenhouse gas (GHG) emissions. As an example, incorporation of renewable power combined with ESS in California could reduce carbon dioxide (CO 2 ) emissions from 90% to 72%, whereas renewable power curtailment reduces from 33% to 9%. In the case of Texas, CO 2 emissions could be reduced from 58% to 54% and renewable power curtailment could be reduced from 3% to 0.3% [4]. Combination of carbon capture and storage devices with conventional generation units is also an option to reduce GHG emissions. However, the combination of renewable generation with ESS can be energetically more effective [5].Historically, the acquisition costs of a battery energy storage system (BESS) have been considerably high, limiting their economic performance and consequently their mass adoption. However, when very low GHG emissions are required, BESS can be a critical device to achieve such an ambitious goal.In the case of energy provision for an isolated hybrid energy system (HES), incorporation of BESS becomes profitable due to the fact that the fuel consumption and operating hours of a conventional generator are considerably reduced. In the case of a grid-connected HES, retailing rates and feed-in tariffs as well as favorable resources are crucial for their successful adoption [6].Heuristic techniques are commonly used to carry out the optimal sizing of a specific HES. Consequently, some of them have been implemented in computational programs such as HOMER Pro ® [7], iHOGA ® [8], and Hybrid2 ® [9], among others. Dispatch strategies implemented in most of these tools are based on load following and cycle charging concepts. Load following consists of generating power from conventional units only to satisfy net load (NL), and this approach is frequently suggested in a HES with high share of renewable power, which is much h...

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Section: Literature Reviewmentioning

confidence: 99%

Day-Ahead Optimal Battery Operation in Islanded Hybrid Energy Systems and Its Impact on Greenhouse Gas Emissions

et al. 2019

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“…For example, Silva et al (2014) perform stochastic optimization to analyze complementarity between wind and hydro power. Jiang et al (2017) use stochastic optimization for day-ahead dispatch scheduling. Deane et al (2013) use stochastic optimization to define operational strategies for pumped-hydro storage systems.…”

Section: Introductionmentioning

confidence: 99%

Repurposing an energy system optimization model for seasonal power generation planning

Queiroz

Mulcahy

Sankarasubramanian

et al. 2019

Energy

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Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty.

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“…In the future, the UC in consideration of voltage stability and transmission network constraints will become necessary because the limitations of the power system will increase and became complicated by the introduction of power liberalization. It had been reported that single purpose including the maximization of the profit in conventional UC, but multi-objective optimization was unknown [16][17][18][19][20][21][22][23]. In this paper, we report multipurpose optimum operation method that minimizes the operational cost and transmission loss in the power system by introducing Electric Vehicle (EV), ESS, and DRP.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal operation with demand management and voltage stability

Furukakoi

Sediqi

Chakraborty

et al. 2017

2017 17th International Conference on Control, Automation and Systems (ICCAS)

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Due to the electric power liberalization, many power producers are entering into the deregulated power market. Most of the electrical power companies are introducing Renewable Energy Sources (RES), which will increase the power system uncertainty. Consequently, the underlyng uncertainty imposed by RES will increase the likelihood of voltage instability and voltage collapse. Also, fluctuations in supply and demand are increased by the introduction of renewable energies. On the other hand, Demand Response Program (DRP) is one of the potential solutions that reduces the power fluctuations. In this study, we proposed multi-objective optimal operation based Unit Commitment (UC) considering voltage stability and demand response. The effectiveness of the proposed method is verified by simulation results using the MATLAB.

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Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system

Cited by 109 publications

References 30 publications

Day-Ahead Optimal Battery Operation in Islanded Hybrid Energy Systems and Its Impact on Greenhouse Gas Emissions

Day-Ahead Optimal Battery Operation in Islanded Hybrid Energy Systems and Its Impact on Greenhouse Gas Emissions

Repurposing an energy system optimization model for seasonal power generation planning

Multi-objective optimal operation with demand management and voltage stability

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