Building‐to‐grid optimal control of integrated MicroCSP and building HVAC system for optimal demand response services

Abstract

The world is shifting toward cleaner and more sustainable power generation to face the challenges of climate change. Renewable energy sources such as solar, wind, hydraulic are now the go-to technologies for the new power generation system. However, these sources are highly intermittent and introduce uncertainty to the power grid which affects its frequency and voltage and could jeopardize its stable operations. The integration of micro-scale concentrated solar power (MicroCSP) and thermal energy storage with the heating, ventilation, and air conditioning (HVAC) system gives the building greater leeway to control its loads which can allow it to support the power grid by providing demand response (DR) services. Indeed, the optimal control of the power flowing between the MicroCSP, the HVAC system, and the thermal zones can bring additional degrees of freedom to the building which can be relegated to the power grid based on the objective function and the incentives provided by the latter. This article presents an in-depth investigation of the MicroCSP potential to provide ancillary services to the power grid. It focuses on evaluating the effect of incentives provided by the power grid on the building participation to the load following programs. It also demonstrates how the MicroCSP can help the building deal with constraints related to load peak shaving and ramp-rate reduction set by the power grid as part of long-term DR contracts. A sensitivity analysis is carried out to confront the results to prediction uncertainties of the energy prices and the weather conditions.

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

“…Buildings-to-power-grid (BtG) and building-to-heating-grid (BtHG) integration systems can simultaneously take into account both the demands on the building side and the grid side [15][16][17][18]. A two-stage robust optimization method for buildings' space heating loads (SHLs) in a BtHG integration system was reported in reference [15].…”

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

“…Hou et al [26] proposed the nonlinear model predictive control to decrease heating costs in university building heating systems while satisfying indoor temperatures. Toub et al [27] proposed a nonlinear model predictive controller to control MicroCSP and building HVAC systems to optimize energy usage and reduce costs. Skrjanc et al [28] designed an internal model controller (IMC) with an internal loop to control the CO 2 level in an indoor environment.…”

“…1. Classification of the control methods in HVAC systems including some of the prior studies[20][21][22][23][24][25][26][27][28][29][30].…”