Reducing CO2 Emissions for PV-CHP Hybrid Systems by Using a Hierarchical Control Algorithm

Abstract

National targets for CO2 reduction in the German building sector have stagnated due to low refurbishment rates. This paper proposes an alternative approach using highly efficient, decentralized energy systems. By combining photovoltaic (PV) systems and combined heat and power (CHP) plants controlled by a modified hierarchical control algorithm, CO2 emissions can be reduced. Results from a single-family home show a 13% CO2 reduction with only 11% higher operational costs on heating days. On summer days, up to 50% CO2 emissions can be avoided without additional costs. The control algorithm easily adapts to changing input parameters, making it suitable for different countries and business cases. Overall, with its modified control, the PV-CHP hybrid system can effectively reduce CO2 emissions and adapt to varying conditions. The control can be easily used for other energy systems, like fuel cells or heat pumps.

Planning of Reserve Storage to Compensate for Forecast Errors

“…The model equations, Equations ( 1)-( 9), are also considered as equality and inequality constraints. However, in (9), only the forecast for the demand P for can be taken into account.…”

“…Therefore, the scheduling problem is solved either for the considered scheduling horizon (e.g., Nemati et al [3], Li et al [4]) or for a receding horizon via model predictive control (e.g., Kaya et al [5]). In the latter one, uncertainties in load forecasts are either explicitly addressed via robust control [6][7][8] or compensated in a subsequent step, e.g., the real-time operation [9,10]. If multiple horizons are considered, such as day-ahead and real-time operation, hierarchical model predictive control is often used [5,9,11].…”

“…In the latter one, uncertainties in load forecasts are either explicitly addressed via robust control [6][7][8] or compensated in a subsequent step, e.g., the real-time operation [9,10]. If multiple horizons are considered, such as day-ahead and real-time operation, hierarchical model predictive control is often used [5,9,11]. In contrast with these scientific efforts, rule-based strategies are often used for implementation.…”

“…Again, most authors focus on electrical systems, and only a few analyze sector-coupled energy systems. For example, [9] investigates a system consisting of a PV and a CHP controlled by a hierarchical control algorithm, and Wang et al [10] analyze multistage energy management for a system consisting of a CHP, electric boiler, PV, and wind turbines and electrical and thermal storage systems.…”