Multiobjective Optimization for Demand Side Management Program in Smart Grid

Li, Dan; Chiu, Wei‐Yu; Sun, Hongjian; Poor, H. Vincent

doi:10.1109/tii.2017.2776104

Cited by 110 publications

(61 citation statements)

References 27 publications

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“…Until now, most papers have focused on the generation side, where the technologies can be categorized as: i) CO2 control devices, such as carbon capture and storage (CCS) [8], [9], ii) renewable-energy-based technologies (RET) [10], [11], iii) emission-constrained models (ECM) [12], [13], and iv) demand-response (DR) based models [14], [15]. The CCS technology is used to capture CO2 from the power plant depositing it in a place where it is not able to enter the atmosphere.…”

Section: B Parametersmentioning

confidence: 99%

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

Pourakbari‐Kasmaei

Lehtonen

Contreras

et al. 2020

IEEE Trans. Ind. Inf.

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There is an increasing concern about controlling and reducing carbon emissions in power systems. In this regard, researchers have focused on managing emissions on the generation side, which is the main source of emissions. Considering emission limits on the generation side results in an increase in locational marginal prices that negatively affects social welfare. However, carbon emissions are a by-product of electricity generation that is used to satisfy the demands on the consumer side. Consequently, demand side emission control may not be achieved if only generation is taken into account. In order to fill this existing gap, in this paper, a demand-side management approach aiming at carbon footprint control is proposed. First, the carbon footprint is allocated among the consumers using an improved proportional sharing theorem method. Each consumer learns about their real-time carbon footprint, excess carbon footprint, and the incurred surcharge tax. Then, demands are adjusted via a proper adjustment procedure. This provides enough information for consumers where demand management may result in carbon footprint and demand reductions as well as the exemption of incurred taxes. Profit analysis for both generation and demand sides is carried out to show the effectiveness of the proposed framework using two illustrative case studies. The results obtained, compared with existing policies such as carbon cap, cap-and-trade, and carbon tax, prove the fairness and the advantages of the proposed model for both the demand and the generation sides.Index Terms-Carbon footprint allocation, carbon abatement, demand side management, power tracing, tax exemption.

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Section: B Parametersmentioning

confidence: 99%

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

Pourakbari‐Kasmaei

Lehtonen

Contreras

et al. 2020

IEEE Trans. Ind. Inf.

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“…DSM is an important component of smart grid technology. DSM refers to programmes, algorithms, and management activities adopted by consumers to optimally utilize the available electricity [12,53]. By implementing an efficient DSM algorithm, the consumers can modify their energy consumption patterns and minimize their electricity bills which can in turn, reduce the production costs of energy.…”

Section: Demand-side Management Classifications and Their Roles In Comentioning

confidence: 99%

A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

Ahmad

Naeem

et al. 2018

Energies

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The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.

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“…To reduce the generation cost, an artificial immune algorithm (AIA) and demand response aggregation (DRA) were proposed to consider hierarchical day-ahead pricing and RESs. The proposed method maximizes net benefits and the social welfare of the customers [35]. However, ToU, CPP, energy exchange, and consumers' privacy are not considered.…”

Section: Energy Management Using Heuristic Optimization Approachesmentioning

confidence: 99%

“…AIA and DRA [35] Maximizes net benefits and social welfare of the customers, and reduces generation cost.…”

mentioning

confidence: 99%

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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Multiobjective Optimization for Demand Side Management Program in Smart Grid

Cited by 110 publications

References 27 publications

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

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