Grey-box models for prediction and control of solar district heat plants

It has become clear that the global energy system needs to shift away from fossil fuels towards clean, renewable energy sources. Experience in the past decades has shown that at-plate solar heat panels can play a role in the energy system of the future, in particular in solar collector fields for district heat generation. This thesis concerns the dynamical modelling of such solar district heat plants (SDHPs). As the upswing of SDHPs occurred rather recently, research on their optimal control is ongoing. The main challenge for control is to adapt to fluctuations in solar radiation and other inputs in an optimal way, ensuring a high and stable outlet water temperature to the grid while minimizing flow fluctuations. Many modelling efforts of single collectors as well as full solar heatfields have been reported, although mostly in the form of detailed physical models.In this thesis, a new approach for describing the dynamics of a large at-plate solar field is proposed. We develop a continuous-discrete stochastic state space model suitable for prediction and control. This model form combines knowledge from physics and information from data, thereby allowing for relatively simple formulations while modelling complex dynamics. Retaining a physically meaningful model formulation has additional advantages for model development, as analysis of residuals and outputs can provide information on suitable model extensions. A basic model was formulated and systematically extended in a forward selection procedure, using a.o. likelihood ratio tests. Themodel development was based on May 2017 measurements from an Aalborg CSP solar heat plant in the municipality of Solrød , Denmark. The models are implemented using the R-package CTSM-R, which includes parameter estimation methods based on maximum likelihood estimation and theextended Kalman filter.It is found that the developed model is suitable for very short term (minutes to hours ahead) to short term (day ahead) prediction, as needed for control and heat output forecasting of a SDHP. It includes several new aspects compared to existing models, the most notable being non-parametric modelling of shading effects and a split of total radiation into diffuse and direct components. Including these elements improves model predictions considerably, and allows for asymmetric panel effciency over the day. Detailed analysis of the model's predictive performance is provided, including a comparison to current ISO standard model and the current Solrød control scheme, as well as a cross-validation on data from different seasons. In future work, the model's performance when using input predictions from weather forecasts should be tested. The model should further be used in a model predictive control scheme in order to improve current SDHP control strategies. This would lead to a smoother pump operation, thereby reducing electricity consumption, costs, and greenhouse gas emissions.<br/

A network-aware market mechanism for decentralized district heating systems

District heating systems become more distributed with the integration of prosumers, including excess heat producers and active consumers. This calls for suitable heat market mechanisms that optimally integrate these actors, while minimizing and allocating operational costs. We argue for the inclusion of network constraints to ensure network feasibility and incentivize loss reductions. We propose a network-aware heat market as a Quadratic Program (QP), which determines the optimal dispatch and a set of nodal marginal prices. While heat network dynamics are generally represented by non-convex constraints, we convexify this formulation by fixing temperature variables and neglecting pumping power. The resulting variable flow heating network model leaves the sign and size of the nodal heat injections flexible, which is important for the integration of prosumers. The market is based on peer-to-peer trades to which we add explicit loss terms. This allows us to trace network losses back to the producer and consumer of these losses. Through a dual analysis we reveal loss components of nodal prices, as well as relations between nodal prices and between seller and buyer prices. A case study illustrates the advantages of the network-aware market by comparison to our proposed loss-agnostic benchmark. We show that the network-aware market mechanism effectively promotes local heat consumption and thereby reduces losses and total cost. We conclude that the proposed loss-aware market mechanism can help reduce operating costs in district heating networks while integrating prosumers. Publication supported by EMB3Rs project

Residential District Heating Network with Peer-To-Peer Market Structure:The Case of Nordhavn District

Over the last decades, district heating has been under development, especially the technologies like heat pumps, solar thermal and cogeneration. However, there is still a long way to go regarding regulation, legislation and market liberalization, which varies across countries and regions. The objective of this work is to investigate the potential benefits of decentralized district heating systems in residential areas. By studying a case study of EnergyLab Nordhavn, a residential area in Copenhagen, Denmark, the paper compares the market outcomes of decentralized systems such as community markets to the centralized pool market currently in practice, under the EMB3Rs platform. The study focuses on key market outputs such as dispatched production, revenues, and daily consumption patterns. Additionally, the paper examines the impact of advanced features such as flexible heat consumption and network awareness in the market. The results of this research suggest that decentralized district heating systems have the potential to improve market outcomes and increase energy efficiency in residential areas.</p

Market Integration of Excess Heat

Excess heat will be an important heat source in future carbon-neutral district heating systems. A barrier to excess heat integration is the lack of appropriate scheduling and pricing systems for these producers, which generally have small capacity and limited flexibility. In this work, we formulate and analyze two methods for scheduling and pricing excess heat producers: self-scheduling and market participation. In the former, a price signal is sent to excess heat producers, based on which they determine their optimal schedule. The latter approach allows excess heat producers to participate in a market clearing. In a realistic case study of the Copenhagen district heating system, we investigate market outcomes for the two excess heat integration paradigms under increasing excess heat penetration. An important conclusion is that in systems of high excess heat penetration, simple price signal methods will not suffice, and more sophisticated price signals or coordinated dispatch become a necessity

onlineforecast: An R package for adaptive and recursive forecasting

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using neural network methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.Comment: 36 page

Onlineforecast: An R package for adaptive and recursive forecasting

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields