scholarly journals Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 862 ◽  
Author(s):  
Ah-Yun Yoon ◽  
Hyun-Koo Kang ◽  
Seung-II Moon

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

2019 ◽  
Vol 13 (3) ◽  
pp. 3274-3282 ◽  
Author(s):  
Hanane Dagdougui ◽  
Ahmed Ouammi ◽  
Louis A. Dessaint

2013 ◽  
Vol 805-806 ◽  
pp. 452-457
Author(s):  
Wen Bo Mao ◽  
Ke Wang ◽  
Jian Tao Liu

A model of continuous optimized power flow (COPF) is proposed, concluding demand response (DR). According to different implementation mechanisms, a series of DR models are built, such as: time of use (TOU), real time price (RTP), critical peak price (CPP), and interruptible load (IL). The influences of these kinds of DR on power system are analyzed, including peak load reduction, cost reduction, and reservation optimization. The results show that: DR can cut the cost, reduce the peak load, and promote the reservation optimization.


2018 ◽  
Vol 8 (1) ◽  
pp. 2621-2626 ◽  
Author(s):  
D. Behrens ◽  
T. Schoormann ◽  
R. Knackstedt

Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.


Author(s):  
Miguel A. Peinado-Guerrero ◽  
Nicolas A. Campbell ◽  
Jesus R. Villalobos ◽  
Patrick E. Phelan

Abstract A framework is proposed for demand-side load management (DSLM) of manufacturers participating in demand response (DR) programs. Utilities are increasingly focused on enticing their portfolios of energy end-users to adjust their energy use patterns in a mutually beneficial manner such as with DR programs. DR programs allow the utility to receive bulk peak load reduction and the participating end-user to receive credit towards their electricity bills. Once an end-user is enrolled in a DR program, they receive periodic requests for some amount of load reduction, typically the day before. Failing to respond to a DR signal will usually cost the end-user handsomely. The end-user is often left to their own discretion on how to attain the level of load reduction requested by the utility. For a manufacturer, this means if the request in load reduction is high enough, they will need to figure out how to curtail production. On the other hand, if the load reduction requested is small enough to need no disruption to production, the utility may be missing out on untapped DR capabilities that could be offered from the ability of the manufacturer to reschedule their production. In either case, the availability of an optimal plan for the manufacturer to best schedule its production in response to a DR event can maximize the benefits for both parties. Most of the research found in literature addresses production scheduling with minimal energy use or cost with respect to a time-of-use price tariff. A system that communicates the desires of the utility to the end-user for a DR event and provides the end-user with support in the decision-making process remains to be developed. The framework proposed addresses these shortcomings, considering the introduction of IoT capabilities and the physical constraints of the manufacturer.


Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1852 ◽  
Author(s):  
Mohammed Olama ◽  
Teja Kuruganti ◽  
James Nutaro ◽  
Jin Dong

Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.


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