This paper presents a constrained multi-objective optimization approach for the simultaneous extension and operation planning of integrated energy networks. The integrated energy network comprises a district heating network, an electric energy network, a gas network, and decentralized heating technologies such as pellet boilers. The approach dissects the area of interest into zones. Each zone is represented by a novel model based on the energy hub concept. Therefore, the approach is scalable and allows for the planning of metropolitan areas. The zones are connected by overlying energy networks. The overlying energy networks and the zones are simulated for one year. The simulation is executed within an optimization. In addition, the approach is capable of accounting for the limits of the integrated energy network’s extension. Consequently, it allows for a realistic planning of future integrated energy networks.

The use of carbon neutral heat sources such as industrial waste heat or large-scale heat pumps requires district heating networks. While automated planning of new district heating networks is well-studied, many metropolitan areas in Central Europe already have existing networks. The main challenge in metropolitan areas is to find the optimal district heating network expansion to connect new consumers to the already existing district heating network. The expansion may lead to overloading of existing pipes or generation units, which has to be considered in the planning process. For larger networks with multiple generation units, ensuring proper operation even during the failure of one generation unit is essential, making resilience a key factor in planning. In this work, we present an algorithm for automated and optimal expansion planning of district heating networks. The algorithm prevents overloads in existing and new pipes and incorporates potential outages of generation units to achieve a resilient network design. The approach was tested on a case study involving more than 3000 consumers and ten generation units with different price assumptions. Results show that the algorithm can compute an optimal expansion within 20 s, without considering resiliency. When considering resiliency, the computation time increases to 81 s or 876 s, depending on the price scenario. Furthermore, the peak demand added by the optimization decreases by up to 97 %. Therefore, considering resiliency has not only a major influence on expansion planning regarding the resulting topology, but also regarding computation time.

Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition's advantage regarding scalability.

Integrating distributed renewable energy sources and time-varying electricity pricing mechanisms creates new optimization opportunities for building energy systems. This paper investigates how different electricity price structures influence the cost-optimal dimensioning and operation of energy infrastructure in residential buildings equipped with photovoltaic systems, heat pumps, battery storage, and thermal buffers. The results imply that, in theory, the building thermal mass offers enough flexibility to benefit from electricity price dynamics and no other storage in form of batteries or thermal buffer is needed. Furthermore, four tariff options are analyzed for their benefits and disadvantages for different building types.

District heating networks play a critical role in the transition of the heating supply of buildings to renewable sources. The transition from coal-fired or gas-fired generation units to heat pumps requires new planning methods for district heating networks, since the efficiency of a heat pump is affected strongly by the supply temperature of the district heating network. Therefore, a co-planning approach including the operation of the district heating network in the planning process is required. This paper presents a novel co-planning approach consisting of two steps. First, an optimal district heating network topology is generated from real geo-referenced data. To determine the optimal topology, a new algorithm designed specifically for district heating networks is presented. Next, a simulation model is automatically generated from the respective topology. An optimization is used for the co-planning approach to select an optimal generation unit, find the optimal supply temperature, and dimension the pipes of the district heating network. In contrast to conventional district heating network planning procedures, the optimization includes a full-year dynamic simulation of the district heating network. The result of the planning process is a full y parameterized district heating network with a matching supply temperature. Furthermore, the use of simulation models allows the results to be reused for sensitivity analyses. This is illustrated by examining the selection of generation units under different CO2 price scenarios.

The increasing electrification in district heating systems through electric heat pumps and the resulting coupling between electrical and heating systems presents challenges to network operators and planners, but it also offers high flexibility potential in distribution network operation. The flexibility offered by electric heat pumps and thermal storages can play a vital role in providing affordable energy storage and the potential for load shifting. However, this flexibility comes with uncertainty as it depends on changing weather conditions and customer behavior. Therefore, the correct sizing of the thermal storage capacities in the planning phase of multi-energy systems (MES) is essential for guaranteeing sufficient flexibility for electrical network operation. Moreover, existing reliability metrics do not capture the interactions between the electrical and thermal domains of MESs. In this paper, a novel methodology is presented for optimal sizing under the uncertainty of thermal storage capacities in a heating network coupled to an electrical network. Distributionally robust chance-constrained optimization (DRCC) is used to model the system to limit the probability of insecure operation due to uncertainty in heat demand forecasting. The proposed approach is demonstrated on a modified MES and the results are compared to those obtained from a conventional deterministic optimization model. A new reliability metric, Expected Heat Not Supplied (EHNS), is introduced to evaluate system reliability. The proposed methodology is designed to provide network planners and operators with the optimal storage capacities needed to balance robustness against existing uncertainties, costs, and system reliability.

In the course of the energy transition, distribution grid operators of heat, gas and power must adapt their grid expansion planning to new challenges. Up to now, distribution grids have mainly been adapted unidirectionally based on the peak loads of consumers. In future grids, flexible consumers, distributed energy generation as well as sector coupling will determine the requirements and constraints for distribution grid planning. In order to integrate the planning of the individual grid operators, this paper proposes an approach in which an additional downstream process step extends the established procedures. The presented concept of coupled planning is aimed to serve as an intermediate stage to pave the way from isolated to fully integrated planning of multi-energy grids. The new process step uses the planning process of the individual organizational bodies and refines it by taking the sector coupling elements into account. This makes it possible to optimize the coupled energy grid and, at the same time, to make optimum use of the planning processes for the individual grids that have already been established and used for many years. The concept has been evaluated and tested with a case example simulation for a typical planning task. The case underlines the benefits of the presented concept. Following the case study the potential of further developing the concept is discussed. The approach is an important step towards comprehensive integrated grid planning and a more efficient, flexible and reliable infrastructure for energy distribution. It results in an economization of resources, specialists and material of the grid operators.

Optical sorting is a key technology for the circular economy and is widely applied in the food, mineral, and recycling industries. Despite its widespread use, one typically resorts to expensive means of adjusting the accuracy, e.g., by reducing the mass flow or changing mechanical or software parameters, which typically requires manual tuning in a lengthy, iterative process. To circumvent these drawbacks, we propose a new layout for optical sorters along with a controller that allows re-feeding of controlled fractions of the sorted mass flows. To this end, we build a dynamic model of the sorter, analyze its static behavior, and show how material recirculation affects the sorting accuracy. Furthermore, we build a model predictive controller (MPC) employing the model and evaluate the closed-loop sorting system using a coupled discrete element–computational fluid dynamics (DEM–CFD) simulation, demonstrating improved accuracy.