Demand response capacity estimation in various supply areas

Shiljkut, Vladimir M.; Rajaković, Nikola

doi:10.1016/j.energy.2015.05.007

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…In this paper, we posited that the load of 240 MW could become controllable and qualify for a DR/DSM program. Results in [12] also show that the capacity of AC devices for DR/DSM in the analyzed EDB supply area has been even greater than the capacity in HS devices. The real CL capacity of AC is, however, significantly smaller.…”

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 73%

“…Namely, a complete switch-off of that load in high summer would lead to an unacceptable rise in indoor temperature. That constraint has limited the available CL capacity within AC devices to the value of around 110 MW, as described in detail in [12].…”

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

“…The method is applicable to the whole utility or to its parts, for any season. Reference [12] presents the analyses of the particular case study for both winter and high summer seasons, showing that the heat storage (HS) devices' capacity within the analyzed EDB supply area was around 240 MW. In this paper, we posited that the load of 240 MW could become controllable and qualify for a DR/DSM program.…”

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

“…An improved comparison of load profiles, used for the CL estimation, is detailed in [12]. The method is based on comparing typical daily load profiles for the same (or close) date in two different years and different weather conditions.…”

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

“…An improved method for estimation of controllable loads is proposed in [12]. The use of buildings themselves for energy storage, with the occupants' comfort as a constraint, is also considered, and a particular DR/DSM program proposed.…”

Section: Introductionmentioning

confidence: 99%

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Long-term forecasting of annual peak load considering effects of demand-side programs

Rajaković

Shiljkut

2017

J. Mod. Power Syst. Clean Energy

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The main purpose of this research paper is to investigate the long-term effects of the proposed demandside program, and its impact on annual peak load forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.

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Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 73%

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

Section: Previous Determination Of Supply Area Controllable Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-term forecasting of annual peak load considering effects of demand-side programs

Rajaković

Shiljkut

2017

J. Mod. Power Syst. Clean Energy

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Multicriteria Decision Analysis for Sustainable Green Financing in Energy Sector

Musti

2023

Sustainable Finance

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Cooperative Operation Schedules of Energy Storage System and Demand Response Resources Considering Urban Railway Load Characteristic under a Time-of-Use Tariff

Kim

Jung

et al. 2021

J. Electr. Eng. Technol.

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This paper proposes an algorithm for the cooperative operation of air conditioning facilities and the energy storage system (ESS) in railway stations to minimize electricity. Unlike traditional load patterns, load patterns of an urban railway station can peak where energy charge rates are not high. Due to this possibility, if applying the traditional peak-reduction algorithm to railway loads, energy changes can increase, resulting in higher electricity bills. Therefore, it is required to develop a new method for minimizing the sum of capacity charges and energy charges, which is a non-linear problem. To get a feasible solution for this problem, we suggest an algorithm that optimizes the facility operation through two optimizations (primary and secondary). This method is applied to the air-quality change model for operating air conditioning facilities as demand-response (DR) resources in railway stations. This algorithm makes it possible to estimate operable DR capacity every hour, rather than calculating the capacity of DR resources conservatively in advance. Finally, we perform a simulation for the application of the proposed method to the operation of DR resources and ESS together. The simulation shows that electricity bills become lowered, and the number of charging and discharging processes of ESS is also reduced.

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Demand response capacity estimation in various supply areas

Cited by 18 publications

References 20 publications

Long-term forecasting of annual peak load considering effects of demand-side programs

Long-term forecasting of annual peak load considering effects of demand-side programs

Multicriteria Decision Analysis for Sustainable Green Financing in Energy Sector

Cooperative Operation Schedules of Energy Storage System and Demand Response Resources Considering Urban Railway Load Characteristic under a Time-of-Use Tariff

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