Assessing the success of electricity demand response programs: A meta-analysis

Srivastava, Aman; Passel, Steven Van; Laes, Erik

doi:10.1016/j.erss.2017.12.005

Cited by 69 publications

(40 citation statements)

References 53 publications

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“…From an economical perspective, Pr e,s t needs to be formulated for the following reasons: (1) decrease the electricity consumption at the peak; (2) increase the electricity consumption at the valley; (3) promote the profitability of the PIES; and (4) reduce the electricity bill of the consumers. In this study, the total daily electricity consumption of PIES is kept unchanged after Pr e,s t is formulated [28]. The reduction in the electricity bill of consumers is used as the constraint, as shown in Equation (10).…”

Section: Construction Of S-p Sub-modelmentioning

confidence: 99%

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Liu

2020

Applied Sciences

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With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term.

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Section: Construction Of S-p Sub-modelmentioning

confidence: 99%

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Liu

2020

Applied Sciences

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“…Several studies have explored the technical potential for residential load shifting and load curtailment to reduce peak demand (Arteconi et al 2013;Bronski et al 2015;and Dyson et al 2014). These studies suggest that demand response can shift up to 20% of the annual electricity demand and 8% of peak demand without compromising comfort and service quality (Bronski et al 2015) although the reproducibility and generalisability of many of these studies is in some doubt (Frederiks et al 2016;Huebner et al 2017;Srivastava et al 2018) (Frederiks, Stenner, Hobman, & Fischle, 2016;Huebner et al 2017;Srivastava, Van Passel, & Laes, 2018).…”

Section: Introductionmentioning

confidence: 99%

Lightening the load: quantifying the potential for energy-efficient lighting to reduce peaks in electricity demand

et al. 2020

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One of the key challenges to greater renewable electricity supplies is the temporal mismatch between non-dispatchable renewable sources and peaks in electricity demand. In addition, increased electrification coupled with the de-carbonisation of electricity generation is likely to increase the scale of demand peaks. This could force investment in carbon-intensive peaker generation or capital intensive storage capacity as well as additional transmission and distribution network capacity which may then be substantially underutilised. Whilst considerable effort has been devoted to testing a range of demand response interventions to reduce or shift consumption, less attention has been given to the ability of certain appliances to permanently reduce demand at peak through energy efficiency. In this paper, we use a published model of future energy-efficient lighting uptake together with multi-year measured lighting demand data from a sample of residential households to model the potential power (MW) and energy (MWh) reductions of a ‘business as usual’ rate of efficient lighting adoption. Our estimates suggest that whilst lighting comprises ~ 4% of overall New Zealand annual electricity consumption, it comprises up to 12% of evening peak electricity consumption in winter. As a result, we estimate that by 2029, more efficient residential lighting could reduce New Zealand’s total annual demand by 1 TWh and reduce the highest winter evening peaks (at 17:00) by at least 500 MW (9%). The winter evening demand reduction would be roughly equivalent to avoiding the need for additional generation capacity of the scale of New Zealand’s Huntly Power Stations 1–4 (coal/gas) plus the Stratford peaker plant (gas open-cycle) and has clear implications for any electricity system that is intending to transition towards ~ 100% renewable generation at least cost.

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“…In contrast to customers with conventional power systems, Microgrid customers play a crucial role by participating in controlling their loads. Consequently, the flexibility from the DR of the demand side would help stabilize the power services and reliability on the power grid systems as well [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

Shin

2020

Energies

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Since the inherent intermittency and uncertainty of renewable energy resources complicates efficient Microgrid operations, a Demand Response (DR) scheme is implemented for customers in the grid to alter their power-usage patterns. However, for a real-time pricing model at the current DR, the automated decision on the energy price is not trustworthy because of artificial interferences to the power generation. As opposed to energy price, an operational cost-based prosumer scheduling approach would be able to protect the integrity of the power grid operations from deceptive market transactions and assist in robust energy management. To investigate the operational challenges associated with the costs and prosumers in the Microgrid, we focus on formulating the problem mathematically and designing approximation algorithms to solve the problem of how to optimally identify suppliers to minimize the total operational costs associated with providing electricity. We prove the hardness of the scheduling as one of the NP-Hard problems and propose polynomial time algorithms for approximating optimal solutions. With a proper resilience level for reliable power services, the scheduling algorithms include ways to construct not only robust supplier networks, but also group energy communities in terms of black start while minimizing the operational costs. A series of theoretical performances and experimental evaluations also demonstrates the practical effectiveness of this scheduling model for the operations.

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Assessing the success of electricity demand response programs: A meta-analysis

Cited by 69 publications

References 53 publications

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Lightening the load: quantifying the potential for energy-efficient lighting to reduce peaks in electricity demand

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

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