Growing renewable energy sources in the electric grid require energy-efficient and flexible consumers. Buildings account for a large share of global electricity demand, and their heating, ventilation, and air conditioning (HVAC) systems offer considerable potential for energy savings through improved control, as well as for load shifting due to their large thermal capacity. While model predictive control (MPC) has been widely studied for improving building performance, real-time implementations that quantify both efficiency and flexibility under practical operational and implementation constraints for complex HVAC systems remain largely unexplored. Therefore, this study presents a real-time-capable MPC application using mixed-integer linear programming (MILP) for a complex all-electric HVAC system with thermally activated concrete slabs, evaluated using a validated simulation model. The controller jointly optimizes electricity consumption under varying electricity tariffs while responding to real-time and 24-h ahead grid signals. Results show average electricity savings of 20.4 % compared to the conventional control, with even greater savings during transitional seasons. In addition, the approach improved compliance with temperature limits. Significant flexibility potential was demonstrated, with the 24-h ahead signal increasing average positive flexibility by 38.0 % compared to the real-time signal. However, estimating flexibility costs proved challenging due to the long thermal memory of the slabs and complex heat transfers. Moreover, real-time constraints limited solver convergence, resulting in potentially suboptimal operation. The findings demonstrate the feasibility of real-time MPC for energy-efficient and grid-responsive HVAC operation under real-world constraints, while highlighting the key limitations and trade-offs encountered in achieving this.

Time series aggregation (TSA) is commonly used in energy system optimization to reduce model complexity and computational expenses by selecting periods to represent the entire time series. TSA’s accuracy has traditionally been assessed by comparing the objective values between the original and TSA models (assumed error). However, evaluating TSA from an investor’s standpoint involves analyzing the performance of TSA-based energy system designs using the original time series. Therefore, we introduce the hidden error and total error, novel error metrics, to evaluate the financial implications of TSA through backtesting the TSA-based system designs using the original time series. Our analysis extends to the effects of carbon removal prices and the choice between marginal and grid-mix emission factors on TSA’s performance. We find that traditional error metrics inadequately capture TSA-induced financial losses. We demonstrate that for ambitious emission reduction targets, the hidden error, which had not been identified previously, is up to 29 times the error identified by prior error evaluation approaches. Consequently, TSA should be applied cautiously, particularly when outcomes are vital for investment decisions and when lower TSA rates yield computationally feasible models, highlighting the need to consider comprehensive error metrics in TSA applications for more accurate energy system optimization.

To address the challenges posed by increasing shares of variable renewable power generation in the electric grid, flexibility procurement platforms are being actively developed. These platforms enable prosumers to offer flexible power for use in mitigating predicted grid congestion. However, the optimal design of such flexibility markets remains unclear and requires thorough analysis. A critical parameter is the lead time between the acceptance of offered flexible power and its delivery, directly influencing flexibility availability and cost. Despite its importance, the impact of lead time on flexibility provision cost has not been evaluated in the literature. In this study, we analyze this cost effect of varying lead times on flexibility provision by simulating a 48-hour moving horizon model predictive control for multiple distributed energy systems on a market platform, delivering flexibility under different lead time scenarios. Additionally, the deliveries are analyzed under varying demand durations, electricity tariffs, daytimes, and seasons to evaluate their response to diverse influencing factors. The findings are presented using a newly developed flexibility heatmap, illustrating lead time dependent flexibility deliveries and their associated costs. The results indicate that with a lead time of 3h, the cost of providing flexibility using current combined heat and power systems is minimized, achieving cost reductions of up to 77%. Transitioning to advanced heat pumps and battery storage technologies increases the available flexibility ninefold. However, such systems require a lead time of 16h to deliver flexibility at minimized costs, highlighting the growing importance of lead time in flexibility provision.

Fifth generation district heating and cooling (5GDHC) networks accelerate the use of renewable energies in the heating sector and enable flexible, efficient and future-proof heating and cooling supply via a single network. Due to their low temperature level and high integration of renewables, 5GDHC systems pose new challenges for the modeling of these networks in order to simulate and test operational strategies. A particular feature is the use of uninsulated pipes, which allow energy exchange with the surrounding ground. Accurate modeling of this interaction is essential for reliable simulation and optimization. This paper presents a thermo-physical model of the pipe connections, the surrounding soil, a latent heat storage in the form of an ice storage as a seasonal heat storage and the house transfer stations. The model is derived from mass and energy balances leading to ordinary differential equations (ODEs). Validation is performed using field data from the 5GDHC network in Gutach-Bleibach, Germany, which supplies heating and cooling to 30 modern buildings. With an average model deviation of 1.7 % in the normalized mean bias error (NMBE) and 13.1 % in the coefficient of the variation of the root mean square error (CVRMSE), the model's accuracy is validated against the available temperature measurements. The realistic representation of the thermal-hydraulic interactions between soil and pipes, as well as the heat flow within the network, confirms the accuracy of the model and its applicability for the simulation of 5GDHC systems. The Modelica implementation of the model is made openly accessible under an open-source license.

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company’s MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of electrical industrial forklifts, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal, electrical, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19%–80% depending on electricity and carbon removal prices), total annual costs, operational carbon footprint, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.

A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF's capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.

Industrial demand response is indispensable for providing operational flexibility to the electricity grid and for integrating the increasing shares of fluctuating renewable energy sources. In Germany and other countries, however, investments and the activation of operational flexibility are inhibited by regulatory systems that were not designed for the renewable energy era but instead, the fossil energy era. Two examples are fixed peak-demand grid fees (FGFs) and the intensive grid usage (IGU) of the Electricity Network Fee Regulation Ordinance in Germany. This paper, therefore, analyzes to what extent the necessary expansion of industrial price-based demand response is hampered by FGFs and IGU using a real-world industrial case study. For this, a design and operation optimization model of a local energy system including a photovoltaic system (PVS) and a multi-use battery energy storage system (BESS) is parametrized with anonymized data of a German chemical plant. The results show that FGFs and IGU hinder the provision of market-serving operational flexibility since flat demand profiles are rewarded. Under current market conditions, FGF and IGU provide investment incentives for BESS but discourage investment in PVSs. For BESS prices of €100kW$\mathrm{h}^{-1}$ and below, FGFs and IGU even impede investments in BESSs.

Multi-modal distributed energy system planning is applied in the context of smart grids, industrial energy supply, and in the building energy sector. In real-world applications, these systems are commonly characterized by existing system structures of different age where monitoring and investment are conducted in a closed-loop, with the iterative possibility to invest. The literature contains two main approaches to approximate this computationally intensive multi-period investment problem. The first approach simplifies the temporal decision-making process collapsing the multi-stage decision to a two-stage decision, considering uncertainty in the second stage decision variables. The second approach considers multi-period investments under the assumption of perfect foresight. In this work, we propose a multi-stage stochastic optimization model that captures multi-period investment decisions under uncertainty and solves the problem to global optimality, serving as a first-best benchmark to the problem. To evaluate the performance of conventional approaches applied in a multi-year setup, we propose a rolling horizon heuristic that on the one hand reveals the performance of conventional approaches applied in a multi-period set-up and on the other hand enables planners to identify approximate solutions to the original multi-stage stochastic problem. We conduct a real-world case study and investigate solution quality as well as the computational performance of the proposed approaches. Our findings indicate that the approximation of multi-period investments by two-stage stochastic approaches yield the best results regarding constraint satisfaction, while deterministic multi-period approximations yield better economic and computational performance.

This work presents the results of experimental operation of a solar‐driven climate system using mixed‐integer nonlinear model predictive control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed‐integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed‐integer nonlinear MPC application.

Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.

This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.

To support the uprise of demand response, especially in the context of industrial processes, we propose a new approach to integrally determine the production-inventory plan and the cost-minimizing bids to participate in sequential reserve and energy-only markets. In particular, our approach considers time-coupling constraints which occur in the context of a production-inventory planning problem. We extend this problem with a comprehensive bidding formulation, which allows evaluating revenues and potential cost from the market participation, considering price uncertainties and uncertain activations of committed reserve capacity. This results in a multistage stochastic mixed-integer linear program, which explicitly considers the stage-wise revelation of information in our setup. To illustrate the capabilities of our approach, we apply our model to a real-world case study in which we investigate the participation of a cement plant in the German energy-only and reserve markets. The results of our case study indicate significant revenues for flexible industrial processes when participating in German spot and reserve markets.

This paper proposes a Mixed-Integer Linear Programming (MILP) formulation for the economic optimization of the synthesis, design, and operation of an energy supply system of a manufacturing company. The multi-period approach incorporates both Heat Upgrading Technologies (HUTs) and conventional Distributed Energy Ressources (DER). Temperature requirements of heating and cooling demands are addressed explicitly and fluctuating ambient temperatures are considered, this gives rise to the possibility of temperature dependent modeling of technology efficiencies. The model enables the planner to consider waste heat recovery from hot process streams or from refrigeration cycles via direct heat integration or HUTs, such as mechanical heat pumps. Furthermore, it enables the planner to evaluate the complex interactions of HUTs with Combined Heat and Power (CHP) plants. To illustrate the practicability of the presented modeling approach, it is applied to a real-world case study. Furthermore, we exemplify how the optimal design is adjusted if HUTs and DER are investigated integrally in contrast to an isolated optimization.

The integration of fluctuating renewable energies leads to higher price fluctuations in day-ahead markets, consequently the incentives for the activation of flexible loads increase. Even if relative forecasting errors decrease, the absolute forecasting error of renewable power production is expected to increase, therefore the demand for reserve power will rise. To fully exploit the economic potential of evolving energy markets through the utilization of production process flexibility, multiple markets need to be considered at the same time. Production planning and the participation in the reserve markets can be formulated as a multistage Stochastic Mixed-Integer Linear Programming (SMILP) problem that minimizes the expected total costs, which consists of cost for purchasing power subtracted by the revenues from offering reserve energy. The presented approach incorporates a production process model which considers uncertainties of spot and reserve market prices in terms of a stochastic process.

Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.