Optimization modeling for dynamic price based demand response in microgrids

Hassan, Muhammad Arshad Shehzad; Chen, Minyou; Lin, Houfei; Ahmed, Mohammed Hassan; Khan, Muhammad Zeeshan; Chughtai, Gohar Rehman

doi:10.1016/j.jclepro.2019.03.082

Cited by 59 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DR is optimized considering the variation of electricity price imposed by DSO to provoke a consumption reduction. In the microgrid environment [22], a PSO is used for solve the DR optimization problem. In this case a dynamic pricing model is considered for increase the profit of costumers.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

et al. 2019

View full text Add to dashboard Cite

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

show abstract

Section: Introductionmentioning

confidence: 99%

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

et al. 2019

View full text Add to dashboard Cite

show abstract

“…However, it does not provide any solution for calculating the MCP [23,24]. Although the DP scheme applied in [25] has managed to maximize the profit of existing agents, such as consumers, by PSO algorithm, the issue of emission reduction has not been investigated. Moreover, the conducted research in [26] shows that utilizing an appropriate DR with a Nash-Stackelberg game approach have led to reaching more utilities for end-users/consumers.…”

Section: Introductionmentioning

confidence: 99%

“…The examination of DR's impact on pollution resulting from generation units and full capabilities of distributed energy resources has not been performed [7][8][9][10][18][19][20][21][25][26][27]29,30,37].…”

mentioning

confidence: 99%

The Impact of Demand Response Programs on Reducing the Emissions and Cost of A Neighborhood Home Microgrid

et al. 2019

View full text Add to dashboard Cite

The desire to increase energy efficiency and reliability of power grids, along with the need for reducing carbon emissions has led to increasing the utilization of Home Micro-grids (H-MGs). In this context, the issue of economic emission dispatch is worthy of consideration, with a view to controlling generation costs and reducing environmental pollution. This paper presents a multi-objective energy management system, with a structure based on demand response (DR) and dynamic pricing (DP). The proposed energy management system (EMS), in addition to decreasing the market clearing price (MCP) and increasing producer profits, has focused on reducing the level of generation units emissions, as well as enhancing utilization of renewable energy units through the DR programs. As a consequence of the nonlinear and discrete nature of the H-MGs, metaheuristic algorithms are applied to find the best possible solution. Moreover, due to the presence of generation units, the Taguchi orthogonal array testing (TOAT) method has been utilized to investigate the uncertainty regarding generation units. In the problem being considered, each H-MG interacts with each other and can negotiate based on their own strategies (reduction of cost or pollution). The obtained results indicate the efficiency of the proposed algorithm, a decrease in emissions and an increase in the profit achieved by each H-MG, by 37% and 10%, respectively.

show abstract

“…Considerable effort has been devoted toward the energy management of a MG by adjusting the DR. In [11][12][13][14], significant effects in terms of reducing the operating cost by incorporating DR was fully analyzed. In [11], a genetic algorithm was adopted to show the effectiveness of the DR in reducing the operating cost of a MG.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], a hierarchical energy management system was proposed to deal with the uncertainty of renewable sources and loads for the optimal operation of a MG. In [13], a multi-agent system was used to optimize the operation of a MG including the DR strategy. In [14], the optimal operation of a multi-MG system incorporating DR was investigated.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response

Kim

2019

Energies

View full text Add to dashboard Cite

This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.

show abstract

Optimization modeling for dynamic price based demand response in microgrids

Cited by 59 publications

References 31 publications

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

The Impact of Demand Response Programs on Reducing the Emissions and Cost of A Neighborhood Home Microgrid

Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response

Contact Info

Product

Resources

About