The increasing share of volatile renewable energy sources requires the development of innovative strategies to address the temporal and spatial mismatches between energy production and consumption. On the production side, predictive, optimization-based methods can be employed to effectively use available flexibility. On the consumption side, Demand Side Management (DSM) offers a promising solution by enabling load shifting, resulting in peak shaving and valley filling. However, integrating DSM into optimization frameworks remains challenging due to the computational complexity growing with the number of consumers. Scalable approaches using price-based mechanisms, on the other hand, often lead to rebound effects or load synchronization, undermining overall system efficiency.This paper introduces a novel distributed method for integrating DSM in the form of Demand Response into highlevel optimization. The objective of this approach is to facilitate an optimal interaction of all controllable generators, storages, and consumers using demand shift potential curves. The method employs a hybrid, non-market-based approach that models flexible demand as a virtual storage system. By using precomputed shift potential curves and coordinating demand adjustments through distributed agents and a central coordinator, the method enables scalable, temporally aware optimization without explicitly modeling each consumer in the optimization problem. This architecture effectively mitigates dynamic inefficiencies associated with traditional DSM methods.The method was validated using a district heating network simulation with 18 heterogeneous residential consumers. Results demonstrate that the proposed method successfully reduced morning peak loads and improved base-load boiler utilization by reshaping demand profiles. Despite minor deviations between optimized and realized shift curves, the method maintained the contractually guaranteed conditions and consumer comfort.

In the transition toward a green energy system, district heating (DH) systems are pivotal in enhancing the flexibility, resilience, and capacity of integrating local and renewable energy sources in urban areas. District heating networks have traditionally been operated with limited controls to ensure the required supply and optimize economic and environmental performance. In recent years, a new digital infrastructure has emerged in response to new policies, and technological advancements in digital solutions are essential to sustain the transition towards a 4th generation district heating (4GDH) system. This review article comprehensively assesses the current landscape and future prospects of digitalization levels in the operation of DH systems. It provides an overview of the latest improvements in digital technologies and their application in optimizing the operation and management of DH networks. The review delves into various aspects, including digital control strategies, data analytics, fault detection and diagnosis, and predictive maintenance with current applications and developments of digital twins and artificial intelligence (AI). The analysis of results in the literature was organized and clustered into specific macro areas: digitalization of the demand side, digitalization at the system level, and digitalization of infrastructure. Furthermore, the study provides an overview of digitalization implementations based on the experiences of early adopters as a benchmark for the replicability and opportunity of new business models.

Many different components for heat or electricity generation have been installed in single-family homes in recent years. The efficient use of the controllable components while harvesting the volatile renewable sources and satisfying the energy consumption has become increasingly difficult. For this, we developed a modular, predictive, optimization-based supervisory control framework now deployed in hundreds of single-family homes. While initially targeting systems with biomass boilers and thermal storage, the focus has recently been extended to heat pumps. However, this shift is not straightforward, since heat pumps are typically paired with smaller buffer sizes, limiting flexibility. At the same time, heat pumps also provide new optimization potential, e.g. making use of varying electricity prices and coefficients of performance. In this contribution, we present results from real-world implementations and discuss the operational differences that arise when transitioning from biomass boilers to heat pumps. The findings demonstrate that optimization strategies must be adapted to system flexibility and storage characteristics, highlighting key requirements for the effective predictive control of residential multi-energy systems.

Building and district energy management increasingly requires advanced control strategies. For several tasks that arise, Machine Learning (ML) and Artificial Intelligence (AI) have found widespread application, but often the interaction with existing elements and the integration into the productive system are done in a rather unsystematic way. Thus, one faces problems concerning interface design, data requirements and reliability/trustworthiness. In order to improve this situation, we outline the general control structure of such systems, review key concepts from ML/AI and discuss approaches for conventional high-level control. This is followed by a literature review on ML/AI applications for energy management. Based on the systematics and the literature findings, we establish a taxonomy for the integration of ML/AI methods for energy management, which leads as a main result to a comprehensive guide for such applications, taking into account both characteristics of the method and the affected elements of the system. This is supplemented by a quick guideline for choice of an appropriate method and for subsequent evaluation. The target readership are control engineers who would like to get a systematic overview of ways to integrate ML/AI methods in their work, and data scientists who want to get a better understanding for main tasks and challenges for applying their tools for control tasks.

Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimization-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalizes undesirable behaviour and minimizes heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) use mainly thermal energy instead of electricity as the driving energy to provide resource-efficient heating and cooling when using waste heat or renewable heat sources. Despite this benefit, AHPDs are still not a very common technology due to their complexity. However, better modulation and part-load capability, which can be achieved through advanced control strategies, can simplify the use of AHPDs and help to better integrate them into complex energy systems. Therefore, this paper presents a new, dynamic model-based control approach for single-stage AHPDs that can extend an AHPD's operating range by employing multi-input-multi-output (MIMO) control methods. The control approach can be used for different AHPD applications and thus control configurations, i.e., different combinations of manipulated and controlled variables, and can also be used for redundantly-actuated configurations with more manipulated than controlled variables. It consists of an observer for the state variables and unknown disturbances, a state feedback controller and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. The proposed control approach is experimentally validated with a representative AHPD for two different control configurations and compared to two benchmark control approaches – single-input-single-output (SISO) PI control representing the state-of-the-art, and model-predictive control (MPC) as an alternative advanced control concept. The experimental validation shows that the two MIMO control approaches (the proposed state feedback and the MPC approach) allow for a wider operating range and hence better part load capability compared to the SISO PI control approach. While MPC generally results in a comparably high computational effort due to the necessity of continuously solving an optimization problem, the proposed state feedback control approach is mathematically simple enough to be implemented on a conventional programmable logic controller. It is therefore considered a promising new control approach for AHPDs with the ability to extend their operating range and improve their part load capability, which in turn facilitates their implementation and thus the use of sustainable heat sources in heating and cooling systems.

Buildings with floor heating or thermally activated building structures offer significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realised with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models of the thermal characteristics of the individual thermal zones exist. Creating these by hand is infeasible for larger numbers of zones; instead, they must be identified automatically based on measurement data. In this paper an approach is presented that allows automatically identifying thermal models usable in MPC. The results show that the identified zone models are sufficiently accurate for the use in an MPC, with a mean average error below 1.5K for the prediction of the zone temperatures. The identified zone models are then used in a distributed optimisation scheme that coordinates the individual zones and buildings of a city quarter to best support an energy hub by flattening the overall load profile. In a preliminary simulation study carried out for buildings with floor heating, the operating costs for heating in a winter month were reduced by approximately 9%. Therefore, it can be concluded that the proposed approach has a clear economic benefit.

District heating (DH) networks have the potential for intelligent integration and combination of renewable energy sources, waste heat, thermal energy storage, heat consumers, and coupling with other sectors. As cities and municipalities grow, so do the corresponding networks. This growth of district heating networks introduces the possibility of interconnecting them with neighbouring networks. Interconnecting formerly separated DH networks can result in many advantages concerning flexibility, overall efficiency, the share of renewable sources, and security of supply. Apart from the problem of hydraulically connecting the networks, the main challenge of interconnected DH systems is the coordination of multiple feed-in points. It can be faced with control concepts for the overall DH system which define optimal operation strategies. This paper presents two control approaches for interconnected DH networks that optimize the supply as well as the demand side to reduce CO2 emissions. On the supply side, an optimization-based energy management system defines operation strategies based on demand forecasts. On the demand side, the operation of consumer substations is influenced in favour of the supply using demand side management. The proposed approaches were tested both in simulation and in a real implementation on the DH network of Leibnitz, Austria. First results show a promising reduction of CO2 emissions by 35% and a fuel cost reduction of 7% due to better utilization of the production capacities of the overall DH system.

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications. (C) 2022 The Authors. Published by Elsevier Ltd.

Both the planning and operation of complex, multi-energy systems increasingly rely on optimization. This optimization requires the use of mathematical models of the system components. The model most often used to describe thermal storage, and especially in the common mixed-integer linear program (MILP) formulation, is a simple integrator model with a linear loss term. This simple model has multiple inherent drawbacks since it cannot be applied to represent the temperature distribution inside of the storage unit. In this article, we present a novel approach based on multiple layers of variable size but fixed temperature. The model is still linear, but can be used to describe the most relevant physical phenomena: heat losses, axial heat transport, and, at least qualitatively, axial heat conduction. As an additional benefit, this model makes it possible to clearly distinguish between heat available at different temperatures and thus suitable for different applications, e.g., space heating or domestic hot water. This comes at the cost of additional binary decision variables used to model the resulting hybrid linear dynamics, requiring the use of state-of-the-art MILP solvers to solve the resulting optimization problems. The advantages of the more detailed model are demonstrated by validating it against a standard model based on partial differential equations and by showing more realistic results for a simple energy optimization problem.

Energy systems have increased in complexity in the past years due to the ever-increasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are optimization-based EMS, which can for example be modelled as stochastic mixed-integer linear programmes (SMILP). Depending on the problem size and control horizon, obtaining solutions for these in real-time is a difficult task. The progressive hedging (PH) algorithm is a practical way for splitting a large problem into smaller sub problems and solving them iteratively, thus possibly reducing the solving time considerably. The idea of the PH algorithm is to aggregate the solutions of subproblems, where artificial costs have been added. These added costs enforce that the aggregated solutions become non-anticipative and are updated in every iteration of the algorithm. The algorithm is relatively simple to implement in practice, re-using almost all of a possibly existing deterministic implementations and can be easily parallelized. Although it has no convergence guarantees in the mixed-integer linear case, it can nevertheless be used as a good heuristic for SMILPs. Recent theoretical results shown that for applying augmented Lagrangian functions in the context of mixed-integer programmes, any norm proofs to be a valid penalty function. This is not true for squared norms, like the squared $$L_2$$ -norm that is used in the classical progressive hedging algorithm. Building on these theoretical results, the use of the $$L_1$$ and $$L_{\infty }$$ -norm in the PH algorithm is investigated in this paper. In order to incorporate these into the algorithm an adapted multiplier update step is proposed. Additionally a heuristic extension of the aggregation step and an adaptive penalty parameter update scheme from the literature is investigated. The advantages of the proposed modifications are demonstrated by means of illustrative examples, with the application to SMILP-based EMS in mind.

Overview on different approaches for the control of the heat distribution networks in case of the integration of large-scale solar thermal systems, and different possibilities for the reduction of the operating temperatures in DH systems.

The control of large-scale solar thermal systems and heating grids respectively hybrid energy systems in which they are embedded, goes along with several control tasks, which are carried out in different control layers. At a higher level, supervisory controllers, often referred to as energy management systems, decide on the operating mode of the different plants and components, and provide the reference signals for their controllers. These modes of operation of the different plants and components are then carried out by the respective controllers at plant and component level, and by those responsible for the operation of the district heating network. The control of large-scale solar thermal systems thus can be divided into the following 3 main categories:

Overview on different approaches for supervisory control strategies, deciding on operating modes and set points for the controls of the different plants and components integrated in solar thermal systems.

The growth of district heating and cooling (DHC) networks introduces the possibility of connecting them with neighbouring networks. Coupling networks can save costs by reducing operating hours of peak load or backup boilers, or free up production capacity for network expansion. Optimization-based energy management systems (EMS) already provide operators of individual DHC networks with solutions to the unit commitment and economic dispatch problem. They are especially useful for complex networks with multiple producers and integrated renewable energy sources, where incorporating forecasts is important. Time-dependent constraints and network capacity limitations can easily be considered. For coupled networks, a centralized optimization would provide a minimum with respect to an objective function which can incorporate fuel costs, operational costs and costs for emissions. However, the individual coupled networks are generally owned by different organizations with competing objectives. The centralized solution might not be accepted, as each company aims to optimize its own objective. Additionally, all data has to be shared with a centralized EMS, and it represents a single point of failure. A decentralized EMS may therefore be a better choice in a multi-owner setting. In this article, a novel decentralized EMS is presented that can handle multi-owner structures with cooperative and non-cooperative coupling. Each local EMS solves its own optimization problem, and an iterative Jacobi-style algorithm ensures consensus among the networks. The distributed EMS is compared to a centralized EMS based on a representative real-world example consisting of three coupled district heating networks operated by two companies.

With the share of renewable energy sources increasing in heating and hot water applications, the role of hydraulic heat distribution systems is becoming more and more important. This is due to the fact that in order to compensate for the often fluctuating behaviour of the renewables a flexible heat transfer must be ensured by these distribution systems while also taking the optimal operating conditions (mass flow, temperature) of the individual components into consideration. This demanding task can be accomplished by independently controlling the two physical quantities mass flow and temperature. However, since there exists an intrinsic nonlinear coupling between these quantities this challenge cannot be handled sufficiently by decoupled linear PI controllers which are currently state-of-the-art in the heating sector. For this reason this paper presents a model-based control strategy which allows a decoupled control of mass flow and temperature. The strategy is based on a systematic design approach from models described in this contribution, which are validated by commercially available components from which most of them can be parametrized by the data sheet. The control strategy is designed for a typical hydraulic configuration used in heating systems, which will allow the accurate tracking of the desired trajectories for mass flows, temperatures and consequently heat flows. The controllers are validated experimentally and compared to well-tuned state-of-the-art (PI) controllers in order to illustrate their superiority and prove their decoupling of the control of mass flow and temperature in real world applications.

.................................................................................................................................................. 9 1. Ausgangslage ................................................................................................................................. 10 1.1. Entwicklungsbild urbaner Energiesysteme ........................................................................... 10 1.2. Urbane Energiesysteme und deren Modellierung ................................................................ 13 1.3. Ökonomie urbaner Energiesysteme ...................................................................................... 14 1.4. Regelung urbaner Energieverbünde ..................................................................................... 15 1.5. Fazit ....................................................................................................................................... 17 2. Projektinhalt .................................................................................................................................. 17 2.1. Definition der Beispielkonfiguration ..................................................................................... 18 2.1.1. Basisszenario ................................................................................................................. 18 2.1.2. Modellierung und Dimensionierung der Photovoltaikanlage ....................................... 19 2.1.3. Erweitertes Szenario ...................................................................................................... 20 2.1.4. Optimierungsbasierte Dimensionierung des thermischen Speichers und der Batterie 21 2.2. Co-Simulations Framework ................................................................................................... 23 2.2.1. Modellierung des beispielhaften Stadtquartiers ........................................................... 27 2.2.2. Modellierung der Energiezentrale ................................................................................. 28 2.2.3. Basisregelung ................................................................................................................. 29 2.2.1. Probleme ....................................................................................................................... 31 2.3. Ökonomisches Bewertungsmodell ........................................................................................ 32 2.3.1. Struktur und Umfang des ökonomischen Basismodells ................................................ 33 2.3.2. Struktur und Umfang des erweiterten ökonomischen Modells .................................... 35 2.3.3. Ökonomisches Gesamtmodell ....................................................................................... 36 2.4. Modellprädiktive Regelung ................................................................................................... 37 2.4.1. Modulare Konfiguration ................................................................................................ 38 2.4.2. Methode zur Lastund Ertragsprognose ....................................................................... 39 2.4.1. Umsetzung der modellprädiktiven Regelung in der Simulation.................................... 40 2.5. Durchgeführte Simulationsstudien ....................................................................................... 41 3. Ergebnisse...................................................................................................................................... 42 3.1. Vergleich aller Simulationsstudien ........................................................................................ 42 3.2. Beispielhafte Ergebnisse aus dem ökonomischen Modell .................................................... 43