scholarly journals Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3701 ◽  
Author(s):  
Haesum Ali ◽  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Jinhong Jeon ◽  
Hak-Man Kim
Keyword(s):  
Demand Response ◽  
Peak Load ◽  
Market Price ◽  
Consensus Method ◽  
Welfare Maximization ◽  
Total Surplus ◽  
Load Demand ◽  
Price Signals ◽  
Demand Response Program ◽  
Demand Response Programs

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.

Demand Response and FERC Mandated Compensation Issues

2012 ◽  
Author(s):  
Donald Lincoln
Keyword(s):  
Energy Efficiency ◽  
Demand Response ◽  
Market Competition ◽  
Peak Load ◽  
Market Price ◽  
Energy Markets ◽  
Local Market ◽  
Wholesale Market ◽  
Market Prices ◽  
Demand Response Programs

This paper describes a Demand Response (DR) pilot event performed at Sandia National Laboratories in August of 2011. This paper includes a description of the planning for the demand response event, sources of energy reduction during the event, the potential financial benefit to Sandia National Laboratories from the event, event implementation issues, and the event results. In addition, this paper presents the implications of the Federal Energy Regulatory Commission (FERC) Order 745, Demand Response Compensation in Organized Wholesale Energy Markets, issued in March 2011. In this order FERC mandates that demand response suppliers must be compensated by the organized wholesale energy markets at the local market price for electricity during the hour the demand response is performed. Energy management in a commercial facility can be segregated into energy efficiency and demand response. Energy efficiency focuses on steady state load minimization. Demand response reduces load for event-driven periods during the peak load. Commercial facility demand response refers to voluntary actions by customers that change their consumption of electric power in response to price signals, incentives, or directions from grid operators at times of high wholesale market prices or when electric system reliability is jeopardized. Demand-response-driven changes in electricity use are designed to be short-term and centered on critical hours during the day when demand is high or when the electricity supplier’s reserve margins are low. Demand response events are typically scheduled between 12:00 p.m. and 7:00 p.m. on eight to 15 days during the hottest period of the year. Analysis has determined that automated demand response programs are more efficient and effective than manually controlled demand response programs due to persistence. FERC has stated that their Order 745 ensures organized wholesale energy market competition and removes barriers to the participation of demand response resources. In Order 745, FERC also directed that the demand response compensation costs be allocated among those customers who benefit from the lower prices for energy resulting from the demand response. FERC has allowed the organized wholesale energy markets to establish details for implementation methods for demand response compensation over the next four years following the final Order issue date. This compensation to suppliers of demand response can be significant since demand response is typically performed during those hours when the wholesale market prices are at their highest levels during the year.

scholarly journals Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yuling Li ◽  
Xiaoying Wang ◽  
Peicong Luo
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Smart Grids ◽  
Peak Load ◽  
Two Stage ◽  
The Sustainable Development ◽  
Second Stage ◽  
Potential Demand ◽  
Electricity Costs ◽  
Demand Response Programs

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

scholarly journals An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone

2019 ◽  
Vol 11 (10) ◽  
pp. 2828 ◽  
Author(s):  
Abdul Conteh ◽  
Mohammed Elsayed Lotfy ◽  
Kiptoo Mark Kipngetich ◽  
Tomonobu Senjyu ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Developing Countries ◽  
Renewable Energy ◽  
Economic Analysis ◽  
Sierra Leone ◽  
Demand Response ◽  
Peak Load ◽  
Developed Countries ◽  
Research Attention ◽  
Load Demand ◽  
The Impact

Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown’s peak load demand data and the price elasticity concept, the interruptible demand response (DR) program has been considered for maximum demand index (MDI) customers. Economic analysis of the energy consumption, customer incentives, benefits, penalties and the impact on the load demand are analyzed, with optimally designed energy management for grid-integrated battery energy storage system (BESS) and photovoltaic (PV)-hybrid system using the genetic algorithm (GA). Five scenarios are considered to confirm the effectiveness and robustness of the proposed scheme. The results show the economic superiority of the proposed DR program’s approach for both customers and supplier benefits. Moreover, RE inclusion proved to be a practical approach over the project lifespan, compared to the diesel generation alternative.

scholarly journals Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets

2020 ◽  
Vol 12 (7) ◽  
pp. 2653 ◽  
Author(s):  
Abdul Conteh ◽  
Mohammed Elsayed Lotfy ◽  
Oludamilare Bode Adewuyi ◽  
Paras Mandal ◽  
Hiroshi Takahashi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Demand Response ◽  
Peak Load ◽  
Load Factor ◽  
Gas Emissions ◽  
Load Profile ◽  
Profile Modification ◽  
Load Demand

Electricity disparity in sub-Saharan Africa is a multi-dimensional challenge that has significant implications on the current socio-economic predicament of the region. Strategic implementation of demand response (DR) programs and renewable energy (RE) integration can provide efficient solutions with several benefits such as peak load reduction, grid congestion mitigation, load profile modification, and greenhouse gas emissions reduction. In this research, an incentive and price-based DR programs model using the price elasticity concepts is proposed. Economic analysis of the customer benefit, utility revenue, load factor, and load profile modification are optimally carried out using Freetown (Sierra Leone) peak load demand. The strategic selection index is employed to prioritize relevant DR programs that are techno-economically beneficial for the independent power producers (IPPs) and participating customers. Moreover, optimally designed hybridized grid-connected RE was incorporated using the Genetic Algorithm (GA) to meet the deficit after DR implementation. GA is used to get the optimal solution in terms of the required PV area and the number of BESS to match the net load demand after implementing the DR schemes. The results show credible enhancement in the load profile in terms of peak period reduction as measured using the effective load factor. Moreover, customer benefit and utility revenues are significantly improved using the proposed approach. Furthermore, the inclusion of the hybrid RE supply proves to be an efficient approach to meet the load demand during low peak and valley periods and can also mitigate greenhouse gas emissions.

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