Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

Abstract

Demand response has been implemented by distribution system operators to reduce peak demand and mitigate contingency issues on distribution lines and substations. Specifically, the campus-based commercial buildings make the major contributions to peak demand in a distribution system. Note that prior works neglect the consumers' comfort level in performing demand response, which limits their applications as the incentives are not worth as compared to the loss in comfort levels for most time. Thus, a framework to comprehensively consider both operating costs and comfort levels is necessary. Moreover, distributed energy resources are widely deployed in commercial buildings such as roof-top solar panels, plug-in electric vehicles, and energy storage units, which bring various uncertainties to the distribution systems, i.e., (i) output of renewables; (ii) electricity prices; (iii) arrival and departure of plug-in electric vehicles; (iv) business hour demand response signals and (v) flexible energy demand. In this paper, we propose an optimal demand response framework to enable local control of demand-side appliances that are usually too small to participate in a retail electricity market. Several typical small demand side appliances, i.e., heating, ventilation, and air conditioning systems, electric water heaters and plug-in electric vehicles, are considered in our proposed model. Their operations are coordinated by a central controller, whose objective is to minimize the total cost and maximize the customers' comfort levels for multiple commercial buildings. A scenario-based stochastic programming is leveraged to handle the aforementioned uncertainties. Numerical results based on the practical data demonstrate the effectiveness of the proposed framework. In addition, the trade-off between the operation costs of commercial buildings and customers' comfort levels is illustrated.

Proactive Resilient Scheduling for Networked Microgrids With Extreme Events

“…where (p, h, w, d) is the set of adaptive second-stage decisions based on first-stage decisions p and uncertainty sets z and O. y is the second-stage decisions, including p, h, w, d. Equation (33) represents the constraints related to only firststage variables (1), (28), (30) and (31), where equation (34) collects constraints (4)- (12) and (15)- (23). Equation (35) accounts for constraints (13) and (26), which involve uncertain RES power generation.…”

“…The historical wind power generation data is from [32]. PEVs and hot water tanks are dispatched to the influential buses as forecasted that may be affected by the extreme event, where those parameters are as from [33]. The parameters of critical power loads are from [34], where non-critical power loads are 10% of the critical power loads.…”

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

“…Through the use of intelligent control techniques, BEMS can be optimized to maximize energy efficiency and integrate RESs while reducing the cost of energy and maintaining the required user satisfaction levels [24], [25]. The advanced control methods not only achieve the desired comfort level but also reduce the operational and maintenance cost, thus improving the energy performance of the building [26], [27].…”

Adaptive Robust Energy Management Strategy for Campus-Based Commercial Buildings Considering Comprehensive Comfort Levels

“…Based on objectives, prior works can be categorized into three different types, namely, (i) minimizing operation and maintenance (O&M) costs of the entire system [1], (ii) maximizing consumers' comfort levels (only focus on the HVAC system) [2], and (iii) minimizing load curtailment costs [3]. Very few of them focus on joint objectives of both minimizing O&M costs and maximizing consumers' comfort levels [4]. This More importantly, commercial buildings (CBs) consume more than 40% of total energy supply to power systems [5].…”