Transactive Control of Coupled Electric Power and District Heating Networks

The aim to decarbonize the energy supply represents a major technical and social challenge. The design of approaches for future energy network operation faces the technical challenge of needing to coordinate a vast number of new network participants spatially and temporally, in order to balance energy supply and demand, while achieving secure network operation. At the same time these approaches should ideally provide economic optimal solutions. In order to meet this challenge, the research field of transactive control emerged, which is based on an appropriate interaction of market and control mechanisms. These approaches have been extensively studied for electric power networks. In order to account for the strong differences between the operation of electric power networks and other energy networks, new approaches need to be developed. Therefore, within this work a new transactive control approach for Coupled Electric Power and District Heating Networks (CEPDHNs) is presented. As this is built upon a model-based control approach, a suitable model is designed first, which enables to operate coupled electric power and district heating networks as efficient as possible. Also, for the transactive control approach a new fitted procedure is developed to determine market clearing prices in the multi-energy system. Further, a distributed form of district heating network operation is designed in this context. The effectiveness of the presented approach is analyzed in multiple simulations, based on real world networks

Distributed Optimization of District Heating Networks Using Optimality Condition Decomposition

The optimal operation of District Heating Networks (DHNs) is a challenging task. Current or future optimal dispatch energy management systems attempt to optimize objectives, such as monetary cost minimization, emission reduction, or social welfare maximization. Typically, this requires highly nonlinear models and has a substantial computational cost, especially for large DHNs. Consequently, it is difficult to solve the resulting nonlinear programming problem in real time. In particular, as typical applications allow for no more than several minutes of computation time. However, a distributed optimization approach may provide real time performance. Thereby, the solution of the central optimization problem is obtained by solving a set of small-scale, coupled optimization problems in parallel. At runtime, information is exchanged between the small-scale problems during the iterative solution procedure. A well-known approach of this class of distributed optimization algorithms is Optimality Condition Decomposition (OCD). Important advantages of this approach are the low amount of information exchange needed between the small-scale problems and that it does not require the tuning of parameters, which can be challenging. However, the DHNs model equation structure brings along many difficulties that hamper the application of the OCD approach. Simulation results demonstrate the applicability range of the presented method

Comparison of discrete dynamic pipeline models for operational optimization of District Heating Networks

Optimal operation of District Heating Networks (DHNs) is a very challenging task. One of the main challenges for DHNs optimization tool designers is the choice of an adequate dynamic thermal pipeline model which gives a good tradeoff between accurately modeling the physics of the thermodynamic processes and simultaneously yielding a numerically efficient model. To address this, the paper states the main Partial Differential Equation (PDE) which is used to describe the convection of hot water throughout the literature, together with reasonable assumptions that lead to minor deviations from measurements. Then, different approaches are described which can be used to solve the respective PDE. More specifically, the very common Node Method (NM), approximations of the NM, the lagrangian approach and different Finite Difference (FD) approaches are presented. The main aim of this work is to provide a qualitative and quantitative comparison of these modeling approaches in the context of optimal DHN operation. Our quantitative results show, that by comparing the different approaches to measurement data, the NM yields the smallest modeling errors for most of the temporal discretization sizes. The qualitative comparison identifies that the lagrangian method lacks the differentiability necessary for the implementation in optimization tools. The advantages of the FD approaches include guaranteeing a fixed number of variables, a constant information depth of the temperature distribution along the pipeline and the simplicity of implementation into optimization tools. The approximations of the NM bring benefits when varying mass flow directions need to be considered, which is a crucial aspect in 4t h generation DHNs

Toward Transactive Control of Coupled Electric Power and District Heating Networks

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.Comment: 35 pages, 16 Figure

Toward Transactive Control of Coupled Electric Power and District Heating Networks

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.Comment: 35 pages, 16 Figure

Distributed Optimization of District Heating Networks Using Optimality Condition Decomposition

The optimal operation of District Heating Networks (DHNs) is a challenging task. Current or future optimal dispatch energy management systems attempt to optimize objectives, such as monetary cost minimization, emission reduction, or social welfare maximization. Typically, this requires highly nonlinear models and has a substantial computational cost, especially for large DHNs. Consequently, it is difficult to solve the resulting nonlinear programming problem in real time. In particular, as typical applications allow for no more than several minutes of computation time. However, a distributed optimization approach may provide real time performance. Thereby, the solution of the central optimization problem is obtained by solving a set of small-scale, coupled optimization problems in parallel. At runtime, information is exchanged between the small-scale problems during the iterative solution procedure. A well-known approach of this class of distributed optimization algorithms is Optimality Condition Decomposition (OCD). Important advantages of this approach are the low amount of information exchange needed between the small-scale problems and that it does not require the tuning of parameters, which can be challenging. However, the DHNs model equation structure brings along many difficulties that hamper the application of the OCD approach. Simulation results demonstrate the applicability range of the presented method