Virtual Power Plants can aggregate and dispatch distributed energy sources (DER) to gain revenue in the Day-Ahead market, however as a Balancing Responsible Party they can risk imbalance cost due to deviations between the Day-Ahead forecast and actual production of their renewable energy portfolio. This study analyses the ability of industrial processes to minimize these imbalances by flexibly adjusting the energy consumption, which is also known as industrial demand response. Firstly, the advantages and disadvantages of major industries to provide demand response in a short-term redispatch scheme were identified by means of a literature survey. Secondly, the capability of the chlor-alkali and hydrogen production industry to minimize imbalances in a Virtual Power Plant was analysed. A simulation setup was developed in Python of a Virtual Power Plant with photo-voltaics (300 MW), onshore wind (300 MW), a chlor-alkali (203.5 MW) and hydrogen plant (193.4 MW) and a controller based on Model Predictive Control. The MPC integrated industrial process dynamics using data-driven Hammerstein-Wiener models which enabled rescheduling of load consumption without violating process constraints. The results show that industrial demand response provided by the chlor-alkali and hydrogen plant can minimize imbalances significantly between -89% to -99%. Furthermore, a sensitivity analysis revealed the effect of important plant and controller parameters on the imbalance minimization which notably indicated that the storage capacity of hydrogen/chlorine and high utilization rate (>95%) of the industrial plants can be a major limiting factor for minimization of imbalances. The findings of this study are expected to contribute to the development of renewable energy based Virtual Power Plants and the wider participation of industrial processes in distributed energy systems.

The energy transition is driving extensive changes to global energy systems, including the electrification of heating and mobility, increased renewable electricity generation, and a shift towards sustainable gases like green hydrogen. These changes will need to be accompanied by major investments in energy infrastructure, especially considering the goals of climate-neutrality envisioned for 2050. In integrated energy system planning, the system-level interactions between, for example, storage, electricity networks and gas networks are taken into account and considered as a whole. This thesis investigates the potential benefits of integrated energy system planning, focusing on the distribution grid level. The analysis is carried out for a case study in the Sterrenburg region of South Holland. Power-gas integration and the integration of electrical energy storage with batteries are investigated using hourly demand and generation profiles based on 2050 scenarios. An expansion planning model is developed to determine the optimal investments for the 2050 scenarios, starting from the existing network. This mixed-integer linear programming model uses PyPSA, an open-source energy system modelling toolbox. The optimisation objective is a combination of investment and operational costs. The results demonstrate spatial and economic benefits from including electrolysis, and limited effects from electric storage and gas-to-power within the scope of the case. There are many possibilities for broadening and deepening the scope in future works. For example, different combinations of integration such as heat networks and long-term gas storage could be considered together.

The thesis mainly concentrates on exploring the suitability and applicability of surrogate models for a multi-carrier energy system (MCES). The climate crisis is being paid more attention owing to its notable effects on the environment globally. For the sake of facing the challenge, the European Union (EU) has developed different strategies. One of which is the “2030 Climate Target Plan” published by the European Commission in Brussel in September 2020. It attempts to achieve a greenhouse gas emissions (GHG) reduction target by at least 55% by 2030. Based on the plan, a list of ideas and definitions centered around integrated energy systems has been proposed. To assess energy system integration strategy in a Dutch context, a generic model of the energy system is necessary. To this end, a detailed representative Dutch electricity distribution network model integrating renewable energy and the heat distribution network model are developed. The simulation of this integrated energy system model is computationally expensive due to the inter-dependencies between various energy sectors, dynamic operation of components within individual energy domain, etc. To overcome the computational burden of detailed models, different machine/deep learning-based surrogate models are established for the electrical network and heating network of the energy system, respectively. They include linear regression model, linear regression with chain model, linear support vector with chain model, decision tree model, random forest model, k-nearest neighbour model, multilayer perceptron model as well as long short-term memory model. Their performances are compared by speed-up factor (SUF) and root mean square error (RMSE), and the best model is selected. The results show linear regression model and long short-term memory model have the best performances in the respective electrical network and heating network of the established energy system.

The Netherlands aims to accelerate the energy transition. Accordingly, ambitious targets have been set for the industrial sector. This will require additional investments in the Dutch industry which is expected to reduce the CO2 emissions at limited costs in comparison with other sectors. However, the ambition to reduce the emissions can create a risk of loss of activity and jobs if the industrial businesses prospects are not ensured. Power-to-heat technology provides an opportunity to the industry to reduce the emissions for heating processes. This technology can be implemented in hybrid configurations to ensure the electrification of heat. Hybrid configurations are characterised by their ability to switch between natural gas and electricity which could cost-effectively reduce the emissions consuming electricity at low prices. In this research, a techno-economic evaluation of hybrid boiler systems is performed to analyse the potential of this technology to cost-effectively reduce the CO2 emissions for steam generation in a production process, in 2030. To this end, the operation of hybrid boiler systems was simulated and assessed. The performance of hybrid configurations was compared to alternative options. Finally, the hybrid systems were analysed under different scenarios for 2030. The results for the case study presented, showed that hybrid configurations saved operation costs and reduced the direct CO2 emissions by almost 20%. Therefore, the hybrid boiler could cost-effectively reduce the emissions. However the potential benefits of hybrid boilers are subjected to variation of electricity, natural gas and CO2 prices.

The concept of microgrid has gained a significant interest of many scholars and engineers worldwide. Microgrid offers substantial benefit such as high reliability, adaptive to disturbance, improved load and generation control and high utilization of renewable energy sources (RES). However, the utilization of renewable energy resources leads to a problem due to its intermittency of the generated power. The conventional solution to measure the intermittency problem, such as network expansion and electrical energy storage, require huge cost and complicated planning. Therefore, the utilization of thermostatically controlled become a more viable solution. Several forms of energies are involved in the industrial microgrid. The utilization of the various form of energies requires a platform that accommodates the co-simulation and data exchange of various models. The aim of this project is to observe the impact of a thermostatically controllable load to the energy saving and to offer a methodology that accommodates the co-simulation and data exchange of various components, such as microgrid network, boilers, and optimization algorithm. Several steps are required in order to achieve the project's goal. Firstly, a comprehensive literature study is conducted to determine the characteristic of the network that has to be modelled. Optimal power flow is used as the optimization algorithm in order to achieve the optimum operation of the system. The second step is performing a series of components modelling. The IEEE14 bus system is selected as a foundation of the network model. A separate boiler model is also developed since in the electrical domain, and it is modelled as a constant power load. A more detailed thermodynamic model is modelled for greater insights. Functional Mock-up Interface platform is used as a standard to accommodates the co-simulation between network model, boiler model, and optimization algorithm. The master code is developed to manage the simulation and data exchange of each component. Finally, a simulation of a different seasonal cycle is implemented to observe the performance of the system. Winter and summer season is chosen as the case scenario due to its different profile on the wind turbines power.