Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage

Abstract

In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.

Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations

“…The case study showed that ADMM algorithm had the faster convergence speed than particle swarm optimization (PSO). van der Heijde et al [30] developed a two-stage optimization algorithm to achieve the optimal design and dispatching of integrated heating system. The genetic algorithm (GA) was adopted to realize the equipment capacity configuration in the first layer, and the optimization solution toolbox of Python was used to achieve the optimal dispatching of the equipment in the second layer.…”

“…Therefore, the two following constraints could be used to ensure the reliability of switching: First, the configuration capacity of CCP is increased to provide sufficient power for the IT equipment and chillers during the pre-cooling including IT, chiller, etc. Equations (30) and (31) can be brought into the configuration model as a constraint to avoid switching faults. Clearly, the above switching logic is based on the fact that the chillers can meet the total cooling load solely.…”

“…As can be seen from Figure 11, there are two system failures that cannot guarantee the normal operation of DCP-IESs, which are caused by the relationship among t UPS , t cs , and T. Parameters t UPS and t cs are calculated by Equations (28) and (29), respectively. Therefore, the two following constraints could be used to ensure the reliability of switching: First, the configuration capacity of CCP is increased to provide sufficient power for the IT equipment and chillers during the pre-cooling time of the absorption chillers, as shown in Equation (30), and second, the configuration capacities of the UPS and cooling storage are increased, as shown in Equation (31).…”

“…where COP 3 is the efficiency of the chiller, LE t is the total electricity load of the DCP-IES at time t, including IT, chiller, etc. Equations (30) and (31) can be brought into the configuration model as a constraint to avoid switching faults. Clearly, the above switching logic is based on the fact that the chillers can meet the total cooling load solely.…”

Reviewing Municipal Energy System Planning in a Bibliometric Analysis: Evolution of the Research Field between 1991 and 2019

“…Section 3.3) deal with this subject. Since 2016, there have always been at least six articles on this topic, including the last nine in 2019: [46][47][48][49][50][51][52][53][54]. Furthermore, the top five of the most relevant journals increasingly deal with district heating in studies on MESP: most frequent subject in Energy Policy and Applied Energy and second most frequent subject in Energy as well as Energies (cf.…”