Risk-Constrained Optimal Energy Management for Virtual Power Plants Considering Correlated Demand Response

Liang, Zheming; Alsafasfeh, Qais; Jin, Tao; Pourbabak, Hajir; Su, Wencong

doi:10.1109/tsg.2017.2773039

Cited by 102 publications

(58 citation statements)

References 20 publications

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“…Since the customer is empowered and plays a key role in smart grid, EMS at the distribution level can monitor control and optimize the local system performance. At the customer end, some of the commonly implemented EMS are SHEMS, HEMS, BEMS, and plant energy management system (PEMS) . At the end users level, the ultimate role of EMS is to minimize energy usage by properly scheduling the devices in specified time horizons.…”

Section: Energy Management Systemmentioning

confidence: 99%

“…It is crucial to understand the motivations of the consumer for engaging in or rejecting a DR program. The financial compensation offered against the inconvenience of users for DR remains insufficient to promote the development of the DR sector . So there is a need for attractive incentive policies for increasing the participation of users. To optimally regulate DER and provide DR, the concept of VPP is proposed in several papers . However, the response from VPP is slow due to the presence of inertia, which needs to be addressed.…”

Section: Critical Reviews and Real‐world Applicationmentioning

confidence: 99%

“…At the customer end, some of the commonly implemented EMS are SHEMS, 24,57 HEMS, 9,24,29,[57][58][59][60][61] BEMS, 15,[62][63][64][65][66] and plant energy management system (PEMS). 67 At the end users level, the ultimate role of EMS is to minimize energy usage by properly scheduling the devices in specified time horizons. The aim of BEMS, HEMS, PEMS, and SHEMS is almost the same, but the functioning and modeling of such EMS programs are different.…”

Section: Types Of Ems At the Distribution Levelmentioning

confidence: 99%

“…• To optimally regulate DER and provide DR, the concept of VPP is proposed in several papers. 227 However, the response from VPP is slow due to the presence of inertia, which needs to be addressed.…”

Section: Dr Implementationmentioning

confidence: 99%

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Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Energy Management Systemmentioning

confidence: 99%

Section: Critical Reviews and Real‐world Applicationmentioning

confidence: 99%

Section: Types Of Ems At the Distribution Levelmentioning

confidence: 99%

Section: Dr Implementationmentioning

confidence: 99%

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Energy management system for smart grid: An overview and key issues

2020

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“…Therefore, demand response (DR) is introduced as a new method to shift the peak loads to off-peak hours or curtail noncritical loads directly to avoid contingency issues on substations and transmission/distribution lines. Based on objectives, prior works can be categorized into three different types, namely, (i) minimizing operation and maintenance (O&M) costs of the entire system [1], (ii) maximizing consumers' comfort levels (only focus on the HVAC system) [2], and (iii) minimizing load curtailment costs [3]. Very few of them focus on joint objectives of both minimizing O&M costs and maximizing consumers' comfort levels [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Energy Management Strategy for Campus-Based Commercial Buildings Considering Comprehensive Comfort Levels

Liang

Bian

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

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Neglecting consumers' comfort always leads to failure or slow-response to demand response request. In this paper, we propose several comprehensive comfort level models for various appliances in campus-based commercial buildings (CBs). The objective of the proposed system is to minimize O&M costs of campus-based CBs and maximize various comfort levels simultaneously under the worst-case scenarios. Adaptive robust optimization (ARO) is leveraged to handle various uncertainties within the proposed system: (i) demand response signals sending from the distribution system operator (DSO); (ii) arrival stateof-charge (SoC) conditions of plug-in electric vehicles (PEVs); (iii) power outputs of renewable energy sources (RESs); and (iv) load demand of other appliances. Benders decomposition, such as column-and-constraint generation (C&CG) algorithm, is used to solve the reformulated NP-hard min-max problem. Extensive simulation results demonstrate the effectiveness of the proposed optimal energy management strategy for campus-based CBs in both minimizing O&M costs and maximizing comprehensive comfort levels.

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Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

Onsomu,

Terciyanlı,

Yeşilata

2024

Engineering Reports

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The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

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Risk-Constrained Optimal Energy Management for Virtual Power Plants Considering Correlated Demand Response

Cited by 102 publications

References 20 publications

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

Adaptive Robust Energy Management Strategy for Campus-Based Commercial Buildings Considering Comprehensive Comfort Levels

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

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