Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications

Feuerriegel, Stefan; Neumann, Dirk

doi:10.1016/j.enpol.2016.05.050

Cited by 52 publications

(10 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first indicator (RF) was expressly defined for the present analysis; the remaining two (ES, PS) were widely used in literature to evaluate the flexibility in the residential sector and to define the political implications of different scenarios in the energy markets [30]. They were also combined with the cost reduction assessment [31] and they were included for defining power storage management strategies [32].…”

Section: Methodsmentioning

confidence: 99%

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

et al. 2021

View full text Add to dashboard Cite

This work aims to evaluate the Flexibility Potential that a residential household can effectively provide to the public grid for participating in a Demand Response activity. In detail, by using 14 dwellings electrical data collection, an algorithm to simulate the Load Shifting activity over the daytime is implemented. That algorithm is applied to different scenarios having considered the addition of several technical constraints on the end users’ devices. In such a way, more realistic demand-side management actions are implemented in order to assess the Flexibility Potential deriving from the loads shifting. Basically, by performing simulations it is possible to investigate how the household appliances real operating conditions can reduce the theoretical Flexibility Potential extent. Starting from a Flexibility Price-Market-based Strategy, this work simulates the shifting over the day and night-time of some flexible loads, i.e., the shiftable and the storable ones. Specifically, all instants where load curtailments and enhancements occur over the typical day, the flexibility strategy effectiveness in terms of percentage, the power and energy that are potentially flexible, are evaluated. All the simulations are performed only for residential consumers to evaluate the actual dwellings Flexibility Potential in the absence of any electrical storage and production systems. The outcomes of these simulations show an average Theoretical Flexibility reduction, which is calculated as the fraction of appliances’ cycles shifting over the total ones, equal to 53%, instead of 66%; in a single dwelling, a maximum variation equal to 29% has been registered. In the end, the monthly average shifted energy per dwellings decreases from 27 to 18 kWh, entailing 32.5% off.

show abstract

Section: Methodsmentioning

confidence: 99%

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recent studies [26][27][28] are exploiting the proneness of LV consumers to adopt DG and DR to analyze and propose better regulatory and policy frameworks. Techno-economic assessment of potential business cases for promoting AD in LV is carried out in other studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…0 ≤ ne ≤ ne max ; (26) in which, the objective function (18) is calculated for all np periods by totaling the maximum cost C p (investment and operational costs), β is a penalty cost that is expected to favor network expansion solutions with zero PNS, P is the real power vector, P G is the real power generation vector, P D is the real power demand vector, Q is the reactive power vector, Q G is the reactive power generation vector, Q D is the real power demand vector, V is the voltage magnitude vector, N is a diagonal matrix with the new equipment in the grid, _ N is the diagonal matrix containing the equipment on the base topology, and ne is the number of reinforcing circuits. It is allowed a maximum of three reinforcements per circuit, ie, ne max = 3.…”

Section: Multiyear Optimization Modelmentioning

confidence: 99%

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Neto

Abaide

Miranda

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a new probabilistic model for active low‐voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33‐Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid.

show abstract

“…To show the technological barrier for DC participation in DR programs, we have considered contemporary DR scenarios, such as those described in reference [12]. The DC should act as a prosumer of electrical energy that has established flexibility purchase contracts with a Flexibility Aggregator in order to alleviate the associated risks with flexibility provisioning to its core business.…”

Section: Introductionmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

View full text Add to dashboard Cite

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

show abstract

Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications

Cited by 52 publications

References 48 publications

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Contact Info

Product

Resources

About