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The aim of this project is to describe the expected evolution of the Spanish electricity sector in the coming decades, as well as to study the economic feasibility of solar PV and CCGT power generation plants in this context. First of all, an analysis of the Spanish electricity market has been carried out. The current energy situation has been also studied, both globally and nationally. Given the growing importance of renewable energies in the sector, the legislative development on renewables has been studied, as well as the impact they have generated in the sector and their different financing mechanisms. Based on the environmental objectives established by the European Union, the general lines of the expected energy transition in the Spanish electricity sector have been defined, estimating the installed capacity of the main generation technologies for the period 2020-2050, as well as their contribution in the generation mix. Likewise, an analysis of the price of electricity in the daily market has been carried out, from which a price prediction model has been developed. According to the results, the weight of renewables will increase progressively during the next decades, reaching in 2050 an approximate share of 90% in the generation mix. The expected load factor for CCGTs, on the other hand, will increase in the mid-term, whereas in the long-term will be reduced below the current values. The price of electricity, on the other hand, will increase significantly between 2020 and 2030, whereas as of this year will undergo a gradual but steady reduction. Given the expected relevance of solar PV and CCGT technologies in the future Spanish generation park, the economic feasibility of both technologies has been studied. In the case of the solar PV plant, an analytical model has been developed in MATLAB by which forecasts of the generation of the plant have been made. These results have been validated by the software SOLAR PV, making a comparison between both results. For, Outgoing

Digital simulation is an effective tool to analyze, design, test and optimize Multi-Vector Energy System (MVES) in a flexible and cost-saving way. This paper explores to realize the joint simulation of multi dynamic physical processes in MVES. The structured modeling technology is adopted to encapsulate each component as a standard module, where the over-detailed distributed-parameter component models are simplified as the input-output characteristic models with lumped parameters. On this basis, a unified solution framework that divides the system simulation model into several parts to avoid directly solving the stiff system equations is developed, which is capable of carrying out generic simulation of any MVES and suitable to provide back-end support for generic simulation platforms. Case studies demonstrate the validity of our modeling and solution methods.

This study presents a management algorithm designed for a Virtual Energy Storage System (VESS), as a result of the EU Horizon 2020 project 'VPP4ISLANDS'. The VESS is an aggregation of distributed flexibility resources in an electricity distribution network, and in the case of this study, it consists of a flexible load, a battery energy storage unit and a diesel generator. The algorithm was designed to control the VESS components in a coordinated manner to effectively act similar to a large capacity battery and compensate for the error in the forecasting of distributed renewable generation. Consequently, the VESS algorithm will be deployed in what the project has named VPP4IBox, assembled by Schneider Electric, to support the integration of more renewable generation in the Turkish energy system. The renewable generation considered in this study consists of a PV system and two wind turbines. Simulation results show that the forecasting error of renewable generation was fully mitigated.

As the demand for electricity and the need for power systems flexibility grow, it is crucial to exploit more reliable and clean sources of energy to produce electricity when needed most. Tidal lagoons generate renewable electricity by creating an artificial head difference between water levels on the seaside, driven by tides, and water levels inside the basin, controlled by flow through the structure. Depending on the level of sea water, power generation from a tidal lagoon can be controlled, i.e. shifting power generation in time. This paper investigates the operation of a tidal lagoon in response to the fluctuating electricity prices. By developing an optimal operation model of the tidal lagoon, its schedule in the day-ahead wholesale electricity market was optimized to achieve the maximum revenue. The Swansea Bay tidal lagoon was used as a case study. It was demonstrated that by exploiting the flexibility offered by tidal lagoon, it can achieve a higher revenue in the day-ahead market, although their total electricity generation is reduced.

The large scale integration of wind generation capacity into an electricity network poses technical as well as economic challenges. In this research, three major challenges introduced by wind including non-correlated power output from geographically dispersed wind farms, wind variability and wind uncertainty were studied. In order to address each of the aforementioned challenges an appropriate modelling approach and case studies were used. The impacts of power output from dispersed wind farms on the Great Britain transmission reinforcement were studied using an optimal DC load flow combined with a power generation model. It was shown that Western and Eastern HVDC links play a crucial role to bypass the Scotland to England transmission bottleneck. The impacts of wind variability on the GB gas and electricity network were investigated through application of the Combined gas and Electricity Network (CGEN) Model. Additional gas storage capacity was shown to be an efficient option to compensate for wind variability. Two-stage and multi-stage stochastic programming models were developed to examine the impact of wind forecast uncertainty on the GB electricity and gas networks. Stochastic modelling approaches were shown to be efficient methods for scheduling and operating the system under wind uncertainty. The key contributions of this thesis are the investigation of the impacts of wind generation variability on the gas network, and development of twostage and multi-stage stochastic programming models of integrated gas and electricity network.

This paper describes a methodology and assumptions used for developing heat decarbonisation scenarios for domestic buildings in urban settings. Compatibility with the UK net-zero goal and the consideration of local opportunities and practicalities are key features of the scenarios. Three cities in South Wales, namely: Cardiff, Newport and Swansea were chosen as case studies. By reviewing policy documents and white papers and consulting with key stakeholders including energy network companies, we identified three high-level scenarios for decarbonising heat in domestic buildings. These scenarios are: 1) ‘Hydrogen’ in which existing gas networks will be used to supply hydrogen to on-gas grid customers for heating, 2) ‘Electrification’ that assumes various types of heat pumps will provide heating to buildings either directly or through district heating networks, and 3) ‘Hybrid’ which consists of balanced share of various electricity and hydrogen for heating. For each of these high-level scenarios, the feasibility of installing appropriate low-carbon heating technologies in buildings was assessed by accounting for the existing infrastructure and the characteristics of the building stock as well as other relevant local circumstances. This determined the number, type and location of buildings installing a certain type of low-carbon heating technology, and therefore gave essential information to carry out further analysis to quantify the impacts of various scenarios on the electricity and gas/hydrogen networks.

A modelling framework was developed for characterising the impacts of variability and uncertainty of renewable generation and load on a multi-vector and multi-scale energy system. A time series synthesis algorithm was proposed to produce a large number of daily profiles for wind, PV and load, representing possible variation features. Based on the synthetic time series, four models with different uncertainty characteristics were built and applied to generate forecast scenarios for wind and PV. Using the generated scenarios, the operation of a combined gas and electricity system was formulated as a two-stage stochastic mixed-integer linear optimisation problem. A simplified Great Britain's energy system was investigated under different flexibility options and uncertainty characterisations. Results indicate that electricity storage was the most effective measure to reduce operation cost. The impacts of uncertainty characterisation methods were significant only when system flexibility was insufficient.

A heat exchanger is an essential component in district heating and cooling networks as it transfers thermal energy from energy centers to substations or customers. Thermal loads are typically supplied by regulating the temperature output of one stream of the heat exchanger and by modifying the mass flow rate of the other one. The availability of dynamic models suitable for control design can aid this process to be performed in an efficient way. In this paper, thermal dynamic models of counter-flow heat exchangers are developed. The models encapsulate key heat exchanger characteristics and consider the dynamic calculation of heat transfer coefficients-which includes the hydraulic behavior of the streams. Although heat exchangers are non-linear systems, linearized models are obtained to design simple yet effective and robust controllers in the frequency domain. These are validated in an advanced process simulation software. It is shown that a good temperature control performance in heat exchangers can be achieved for a wide range of operating conditions with a simple PI controller.

Current energy transitions towards the use of more distributed generation, as well as the decarbonisation of heat and transport, are changing the operation of local energy distribution systems. The impact of these local changes on a national scale energy supply system is not well understood. An energy hub approach was integrated into a national scale gas and electricity transmission networks model (CGEN), to represent local energy distribution systems. The energy hub models the integrated operation of electricity, natural gas and heat distribution systems. The distribution system within a region is described in terms of energy supply sources, conversion technologies and storage systems. Transmission supply points link the energy hubs with the gas and electricity transmission networks. A case study was conducted to investigate the impacts on model outputs by integrating energy hubs into the CGEN model. Preliminary results indicate that the operation of distributed generation and storage in energy hubs have a direct impact on electricity and natural gas supply in the transmission networks. The proposed methodology, therefore, extends the analytical capability of the CGEN model across multiple scales and vectors including heat.

Combined heat and power (CHP) units can be operated flexibly to mitigate the impact of renewable resources. However, when used in district heating systems, the constraint governing heat and electricity outputs limits the capability of CHP in supporting the power grid. To addresses this issue, this paper presents a two-layer control system to coordinate CHP, district heating network and buildings. With this control system, CHP can meet the heat demand of local users and provide frequency response for the power grid simultaneously. The excess/deficit heat of CHP supply is balanced by heat stored in buildings and pipelines. The proposed method is tested in the GB Firm Frequency Response market. Numerical results show that the capability of CHP in providing frequency response is determined by the following factors: capacity and ramping rate of CHP, the restriction in the supply temperature of district heating network and the temperature of buildings.

In this paper, we aim to give a brief introduction to what flexibility on electricity system means in the UK, before focussing the investigation to steel making industries and explore the potential of flexibility from steelworks. An up-to-date review of ancillary services needed by the National Grid in the UK is also given. Finally, we discuss feasible ancillary services for steelworks to participate in order to make revenues, so as to address how flexibility from steelworks industries can be best valued.

The employment of battery storage is recognized to be a solution for managing the variability of renewable energy sources in power systems. In this paper the feasibility of integrating a battery energy storage system (BESS) into a renewable energy park was investigated. The energy park consists of three wind turbines with a total generating capacity of 6MW and 2MW of solar photovoltaic panels. A maximum power export limit of 5 MW to the grid is enforced. The optimal size of the BESS was calculated to maximize the profit made by the energy park through participating in the electricity spot market. To do this, a linear optimization model embedded within a net present cash flow approach was developed. Optimal size of the battery storage capacity and optimal power rating of the inverter for different battery costs assumptions were examined.

Heat exchangers have a predominant role in heat transfer and constitute a key component of integrated energy systems. For instance, heating and cooling of fluids are the cornerstone of district heating systems. Thus, it is fundamental to understand the heat exchanger's dynamic behavior to design controllers which are robust to temperature variations. Following this line, this paper presents a dynamic model of a plate type heat exchanger. The model is obtained from first principles of energy transfer and fluid mechanics and describes the thermal behavior of fluids involved in the heat transfer process. Although the system is non-linear, system linearization is performed to obtain a frequency domain model suitable for control system design. The linear model is implemented in MATLAB to design a controller that regulates the cold stream outlet temperature. To assess its effectiveness, the controller is discretized, programmed in Python and implemented in Apros - an advanced process commercial simulation software. Simulation results show that the controller is capable of tracking reference temperatures while ensuring a good performance of the heat exchanger system.

Large capacity of wind generation is expected to be installed across Great Britain by 2020. Wind generation is variable, therefore fast ramping generation plants will be required to balance electricity demand. Gas-fired generation plants will most likely be used to compensate for wind generation variability because of their ramping capability and large installed generation capacity. This will cause comparable gas demand swings in the gas network as wind varies. A combined gas and electricity network optimisation model (CGEN) is used to quantify impacts of wind variability on gas network operation. Analysis is performed on mitigation measures such as greater dual fuel capable gas-fired power plants and gas storage capacity in order to reduce gas network constraints and provide alleviation against wind generation variability.

Large capacity of wind generation is expected to be installed across Great Britain by 2020. Wind generation is variable, therefore fast ramping generation plants will be required to balance electricity demand. Gas-fired generation plants will most likely be used to compensate for wind generation variability because of their ramping capability and large installed generation capacity. This will cause comparable gas demand swings in the gas network as wind varies. A combined gas and electricity network optimisation model (CGEN) is used to quantify impacts of wind variability on gas network operation. Analysis is performed on mitigation measures such as greater dual fuel capable gas-fired power plants and gas storage capacity in order to reduce gas network constraints and provide alleviation against wind generation variability.

Large capacity of wind generation is expected to be installed across Great Britain by 2020. Wind generation is variable, therefore fast ramping generation plants will be required to balance electricity demand. Gas-fired generation plants will most likely be used to compensate for wind generation variability because of their ramping capability and large installed generation capacity. This will cause comparable gas demand swings in the gas network as wind varies. A combined gas and electricity network optimisation model (CGEN) is used to quantify impacts of wind variability on gas network operation. Analysis is performed on mitigation measures such as greater dual fuel capable gas-fired power plants and gas storage capacity in order to reduce gas network constraints and provide alleviation against wind generation variability.