scholarly journals Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4567
Author(s):  
Julian Garcia-Guarin ◽  
David Alvarez ◽  
Arturo Bretas ◽  
Sergio Rivera
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Energy Resources ◽  
Minimum Size ◽  
Normal Operation ◽  
Schedule Optimization ◽  
Load Demand ◽  
Potential Benefits ◽  
Residential Demand ◽  
Ranking Index

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

scholarly journals Building a Better Baseline for Residential Demand Response Programs: Mitigating the Effects of Customer Heterogeneity and Random Variations

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 570
Author(s):  
Peter Schwarz ◽  
Saeed Mohajeryami ◽  
Valentina Cecchi
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Peak Time ◽  
Clustering Methods ◽  
Independent System ◽  
Grid Systems ◽  
Customer Heterogeneity ◽  
Novel Method ◽  
Residential Demand ◽  
Demand Response Programs

Peak-time rebates offer an opportunity to introduce demand response in electricity markets. To implement peak-time rebates, utilities must accurately determine the consumption level if the program were not in effect. Reliable calculations of customer baseline load elude utilities and independent system operators, due to factors that include heterogeneous demands and random variations. Prevailing research is limited for residential markets, which are growing rapidly with the presence of load aggregators and the availability of smart grid systems. Our research pioneers a novel method that clusters customers according to the size and predictability of their demands, substantially improving existing customer baseline calculations and other clustering methods.

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

2015 ◽  
Vol 138 ◽  
pp. 695-706 ◽  
Author(s):  
Qi Wang ◽  
Chunyu Zhang ◽  
Yi Ding ◽  
George Xydis ◽  
Jianhui Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Electricity Markets ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy

Role of residential demand response in modern electricity markets

2014 ◽  
Vol 33 ◽  
pp. 546-553 ◽  
Author(s):  
Matteo Muratori ◽  
Beth-Anne Schuelke-Leech ◽  
Giorgio Rizzoni
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Residential Demand

scholarly journals On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

2020 ◽  
Vol 35 (2) ◽  
pp. 840-853 ◽  
Author(s):  
Kenneth Bruninx ◽  
Hrvoje Pandzic ◽  
Helene Le Cadre ◽  
Erik Delarue
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Residential Demand

Energy management of a microgrid using demand response strategy including renewable uncertainties

2021 ◽  
Vol 22 (1) ◽  
pp. 85-100
Author(s):  
Suchitra Dayalan ◽  
Rajarajeswari Rathinam
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Effective Means ◽  
Operating Cost ◽  
Energy Resources ◽  
Response Strategy ◽  
Distributed Energy ◽  
Real Time Control ◽  
Minimum Operating

Abstract Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.

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