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5th generation district heating and cooling networks operating at near ground temperature offer a low-cost, zero-carbon energy solution. Detailed understanding and accurate estimation of ground behaviour for its heat storage and recharge potential are of paramount importance for the success of such networks. In this paper, an advanced modelling tool, based on a coupled Thermal-Hydraulic (TH) modelling framework, is presented to calculate and predict temperature and soil-moisture behaviour of a shallow ground under complex atmospheric, temperature and hydraulic boundary conditions. Atmospheric data e.g., solar radiation, rainfall, humidity, air temperature, wind velocity is considered together with subsurface soil data to investigate thermal and hydraulic responses of the ground, and its individual soil layers. Furthermore, a transient method for estimating shallow ground source heat (SGSH) resources is proposed based on the simulated temperature and saturation distributions of the ground. The model is applied to predict the long-term ground temperature and saturation level of a test site located in Warwickshire County, UK. The total heat content per unit area and the annual/seasonal/monthly net heat content per unit area of the site are predicted for a five-year period. The total heat content of the sandy clay layer varied between 2.32 and 11.6 MJ/m2, silty clay from 34.0 to 50.5 MJ/m2, and mudstone from 50.7 to 55.0 MJ/m2. A parametric sensitivity study is also conducted to investigate the effects of soil types and hydraulic drainage conditions on the ground heat supply potential, and it revealed that the spatial and temporal distributions of ground heat is significantly affected by the underlying soils. This study highlights the influences of atmospheric conditions and coupled ground processes, and the parameters that should be considered for designing a 5th generation low-temperature heat network.

This paper addresses the cyber resilience issues of multi-vector energy distribution systems (MEDS) caused by false data injection FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The first stage determines an initial plan for day-ahead reserve preparation and the second stage makes adjustment and takes resilience-based actions after potential load redistribution (LR) attacks and renewable output deviation. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, i.e., a 33-bus-20-node MEDS and a 69-bus-20-node-MEDS, illustrating the effectiveness of the proposed cyber-secured model.

Several sources of revenue are available for battery storage systems that can be stacked to further increase revenue. Typically, price arbitrage is used to gain revenue from battery storage. However, additional revenue can be gained from participation in ancillary services such as frequency response. This study presents a linear optimisation approach to account for local energy system participation in the wholesale day-ahead electricity market and multiple frequency response services. The methodology was applied to a school case study. A breakdown of market revenue and value of investment is presented for five operating strategies. The value of availability revenue and response energy revenue are distinguished for frequency response services. Finally, the impact of revenue stacking on battery degradation is assessed. The results show that local energy systems can decrease their operating costs and improve battery storage investment viability by stacking multiple revenues, whilst reducing degradation and increasing lifetime.

Electricity Systems in europe are experiencing major changes due to targets for renewable energy integration, reducing greenhouse gas emissions, and energy efficiency. Different studies show that there is a growing need for more flexibility and active stakeholder involvement at all levels (from small consumers to pan-European networks) to ensure the efficient and reliable operation of the electricity system, particularly to deal with growing volumes of renewable energy sources, from transmission-level wind and solar farms to household-level photovoltaic generation. Other key evolutions that aim to decarbonize the energy sector beyond electricity, such as those based on the electrification of energy end uses (e.g., the development of electric vehicles and the electrification of heating), are also expected to have a substantial impact.

With more variable and uncertain patterns of electricity production and consumption, the need for flexibility in the power grid is becoming increasingly crucial. Industrial energy systems have the potential to contribute to providing such flexibility. Yet, there is still a lack of effective methods to quantify the magnitude of available flexibility from industrial energy systems that can be optimally dispatched to support the operation of the power grid. This paper studies the flexibility provision from steam systems, which exist in many energy-intensive industries. A generic model of industrial steam systems with turbine-generators is presented to reflect its interactions with the power grid. Then, a hybrid physics-based and data-driven approach is developed to approximate the boundaries of the flexibility domain at different operating conditions of the steam systems. The proposed flexibility quantification method is applied to two real industrial steam systems in a paper mill and a steel mill. The results show that the proposed method can approximate the flexibility boundaries under uncertainty steam states and reflect key factors that affect the boundaries. Also, it is shown that neglecting the limits imposed by the steam network leads to an overestimation of flexibility boundaries at certain operating conditions.

The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of individual prosumers in terms of identifying idiosyncratic pricing patterns, cost-effectively predicting power profiles, and scheduling various scales of generation and consumption sources. To overcome these three challenges, this study proposes a novel data-driven energy scheduling model for an individual prosumer. The pricing patterns of a prosumer are represented by three types of dynamic price elasticities, i.e., the price elasticities of the generation, consumption, and carbon emissions. To improve the computational efficiency and scalability, the heuristic algorithms used to solve the optimisation problems is replaced by the convolutional neural networks which map the pricing patterns to scheduling decisions of a prosumer. The variations of uncertainties caused by the intermittency of renewable energy sources, flexible demand, and dynamic prices are predicted by the developed real-time scenarios selection approach, in which each variation is defined as a scenario. Case studies under various IEEE test distribution systems and uncertain scenarios demonstrate the effectiveness of our proposed energy scheduling model in terms of predicting scheduling decisions in microseconds with high accuracy.

Traditionally, power system operation has relied on supply side flexibility from large fossil-based generation plants to managed swings in supply and/or demand. An increase in variable renewable generation has increased curtailment of renewable electricity and variations in electricity prices. Consumers can take advantage of volatile electricity prices and reduce their bills using electricity storage. With reduced fossil-based power generation, traditional methods for balancing supply and demand must change. Electricity storage offers an alternative to fossil-based flexibility, with an increase expected to support high levels of renewable generation. Electrochemical storage is a promising technology for local energy systems. In particular, lithium-ion batteries due to their high energy density and high efficiency. However, despite their 89% decrease in capital cost over the last 10 years, lithium-ion batteries are still relatively expensive. Local energy systems with battery storage can use their battery for different purposes such as maximising their self-consumption, minimising their operating cost through energy arbitrage which is storing energy when the electricity price is low and releasing the energy when the price increases, and increasing their revenue by providing flexibility services to the utility grid. Power rating and energy capacity are vitally important in the design of an electricity storage system. A case study is given for the purpose of providing a repeatable methodology for optimally sizing of a battery storage system for a local energy system. The methodology can be adapted to include any local energy system generation or demand profile.

This work assesses the potential of surplus electricity in reducing greenhouse gas (GHG) emissions in the building sector. The assessment is done by modelling the interaction of government and energy consumer using game theory. The government can provide discounted power to energy consumer by covering a fraction of the off-peak price to encourage the replacement of natural gas consumption with electricity. This replacement reduces GHG emissions from the building sector. Energy consumer adopts electricity-based technologies only if it leads to a lower heat and electricity supply cost. Cost-effectiveness of solid oxide fuel cell, air–source heat pump (ASHP), and battery and hydrogen storage are assessed as alternatives to natural gas combined heat and power (CHP) and boiler technologies. The modelling results show that ASHP is the only technology that can compete with natural gas CHP and boiler. ASHP is chosen by the energy consumer when discounts of 4.5 cents/kWh or more for off-peak electricity are available. The analysis also showed that CHP could be completely replaced by grid power at discount value of 4.5 cents/kWh and up. Natural gas boilers continue playing a role in building heating supply even under increased discount for off-peak electricity price.

Large and reliable volumes of water are required to cool thermal power plants. Yet across the world growing demands from society, environmental regulation and climate change impacts are reducing the availability of reliable water supplies. This in turn constrains the capacity and locations of thermal power plants that can be developed. The authors present an integrated and spatially explicit energy systems model that explores optimal capacity expansion planning strategies, taking into account electricity and gas transmission infrastructure and cooling water constraints under climate change. In Great Britain, given the current availability of freshwater, it is estimated that around 32 GW of combined cycle gas turbine capacity can be sustainably and reliably supported by freshwater. However, to maintain the same reliability under a medium climate change scenario, this is halved to 16 GW. The authors also reveal that the current benefit of available freshwater to the power sector is ∼£50 billion between 2010 and 2050. Adapting to expected climate change impacts on the reduced reliability of freshwater resources could add an additional £18–19 billion in system costs to the low-carbon energy transition over the time horizon, as more expensive cooling technologies and locations are required.

Heat consumption management and effective temperature control strategies to meet heat demand in residential and office buildings have become an important aspect within energy management. A thermal dynamic model of a building is not only necessary to estimate the energy consumption under different operating conditions but also to design effective controllers. This paper presents a classical control approach for the indoor temperature regulation of buildings. State-space and transfer function models of house thermal behaviour are developed. These are obtained from first principles of heat transfer and their analogy with electrical systems. To capture a realistic behaviour of heat transfer, the proposed models consider parametric uncertainties. A frequency response-based approach is used to obtain a reduced order system that facilitates control system design. The models have been implemented in MATLAB/Simulink and a PI controller has been designed to maintain a comfortable indoor temperature in the building. Simulation results show that the controller effectively regulates temperature despite system disturbances. An energy saving of around 8% comparing the proposed controller to a traditional on/off controller is achieved.

District heating systems (DHS) provide thermal energy to a range of consumers. Hence, an adequate sizing of the key elements involved in the energy supply system and their management are critical. Pumps and valves are essential components of a DHS as they ensure hydraulic operating conditions are met for the energy distribution process. To achieve this, a hydraulic system is typically controlled by defining a differential pressure set‐point at a critical location in the network. However, a good understanding of the dynamic behaviour of the hydraulic system during the diverse operating conditions is required for its efficient control and to maximise its performance. This paper presents a control strategy based on suitable dynamic models of the hydraulic system. These non‐linear models enable the simulation of the behaviour of mass flow rate, pressure drops in pipes, power consumption of the pump and the heat delivery to meet the thermal loads. Control system design is carried out in MATLAB, and the designed controller is verified with Apros—a commercial process simulation software. It is shown that the hydraulic behaviour of a DHS is well described by the dynamic models presented. In addition, the designed control scheme reduces the electricity consumption of pumps compared with a conventional mass flow rate controller based on a look‐up table and a differential pressure valve.

A method for estimating the heat demand of different types of dwellings is described. Furthermore, an optimal heat supply mix for a local area was determined considering gas and electricity prices and the unique characteristics of the local area in terms of the heat demand and potential heat supply options. These methods were demonstrated on the local authority area of Neath Port Talbot in the UK. The estimated heat demand was validated against real data. The modelling results obtained show that significant financial support is required for low carbon heating technologies such as heat pumps and district heating networks for them to play a major role in the decarbonisation of heat.

[No abstract available]

Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

District heating systems (DHS) that generate/consume electricity are increasingly used to provide flexibility to power grids. The quantification of flexibility from a DHS is challenging due to its complex thermal dynamics and time-delay effects. This article proposes a three-stage methodology to quantify the maximum flexibility of a DHS. The DHS is firstly decomposed into multiple parallel subsystems with simpler topological structures. The maximum flexibility of each subsystem is then formulated as an optimal control problem with time delays in state variables. Finally, the available flexibility from the original DHS is estimated by aggregating the flexibility of all subsystems. Numerical results reveal that a DHS with longer pipelines has more flexibility but using this flexibility may lead to extra actions in equipment such as the opening position adjustment of valves, in order to restore the DHS to normal states after providing flexibility. Impacts of the supply temperature of the heat producer, the heat loss coefficient of buildings and the ambient temperature on the available flexibility were quantified.

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.

The increased interdependencies between electricity and gas systems driven by gas-fired power plants and gas electricity-driven compressors necessitates detailed investigation of such interdependencies, especially in the context of an increased share of renewable energy sources. In this paper, the value of an integrated approach for operating gas and electricity systems is assessed. An outer approximation with equality relaxation (OA/ER) method is used to deal with the optimization class of the mixed-integer non-linear problem of the integrated operation of gas and electricity systems. This method significantly improved the efficiency of the solution algorithm and achieved a nearly 40% reduction in computation time compared to successive linear programming. The value of flexibility technologies, including flexible gas compressors, demand-side response, battery storage, and power-to-gas, is quantified in the operation of integrated gas and electricity systems in GB 2030 energy scenarios for different renewable generation penetration levels. The modeling demonstrates that the flexibility options will enable significant cost savings in the annual operational costs of gas and electricity systems (up to 21%). On the other hand, the analysis carried out indicates that deployment of flexibility technologies appropriately supports the interaction between gas and electricity systems.

The growing interdependencies between natural gas and power systems, driven by gas-fired generators and gas compressors supplied by electricity, necessitates detailed investigation of the interactions between these vectors, particularly in the context of growing penetration of renewable energy sources. In this research, an expansion planning model for integrated natural gas and power systems is proposed. The model investigates optimal investment in flexibility options such as battery storage, demand-side response, and gas-fired generators. The value of these flexibility options is quantified for gas and electricity systems in Great Britain in 2030. The results indicate that the flexibility options could play an important role in meeting the emission targets in the future. However, the investment costs of these options highly impact the future generation mix as well as the type of reinforcements in the natural gas system infrastructure. Through the deployment of the flexibility options up to £ 24 .2 billion annual cost savings in planning and operation of natural gas and power systems could be achieved, compared to the case that no flexibility option is considered.

Due to the increasing use of intermittent renewable generation, the power grid requires more flexible resources to balance supply and demand of electricity. Steam systems with turbine-generators, which are widely used in industries, can be operated flexibly to support the power grid. Yet, the available amount of flexibility of industrial steam systems is still not clearly quantified. This study presents the method to quantify electricity generation flexibility of a typical industrial steam system with a steam turbine-generator and process heat demands. The proposed method is introduced based on a real case of an integrated pulp and paper mill in Austria. An integrated mathematical model representing the combined electricity and steam system is developed to simulate the behaviour of the on-site energy system to quantify the potential flexibility provision. Flexibility is represented as the maximum upward and downward changes in the imported electricity from the public power grid. The results demonstrate that it is possible to aggregate the flexibility of the industrial facility as a lookup table. Also, the results reflect key factors that limit the flexibility at different operating points of the turbine-generator.