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Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed. [ABSTRACT FROM AUTHOR]

Wales is committed to meeting the Net-zero emissions target by 2050. To meet this challenge unprecedented changes in the energy system are required in South Wales. There are various pathways to achieve net-zero emissions in South Wales. These pathways are usually compared based on their costs. However, energy supply reliability assessment is required to determine the security of these scenario pathways. A probabilistic dynamic reliability assessment framework is proposed, which combines the Improved Universal Generating Function and the Improved Fisher optimal algorithm. This technique reduces the computational burden of reliability assessment by 95% with a similar accuracy compared with Monte Carlo Simulations. The impacts and sensitivities of energy sources and technologies on supply reliability in High Electrification and High Hydrogen scenarios are measured. As the penetration level of local renewables increases five-fold in 2050 compared with 2020, Loss of Load Expectation increases from 3 to 10–12 h/year in High Electrification and High Hydrogen scenarios. In summer, the reliability of energy supply is more sensitive to changes in the assumptions on failure probabilities of energy conversion devices, whereas the system's reliability in winter is mainly impacted by the availability of imported energy to South Wales. • PMFs of variables obtained from historical data are used in the Improved UGF. • A systematic dynamic reliability assessment framework is developed. • Energy supply reliability across distinct net-zero energy futures is investigated. [ABSTRACT FROM AUTHOR]

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the potential role of the electricity interconnectors in improving the security of supply in Great Britain (GB) in 2030. Real electricity demand and price data for GB and France in 2016 were used to understand the relationship between power exchange between the two countries and their wholesale electricity prices. A linear programming optimisation model was developed to find the economic power dispatch. Two interconnection links were considered; two‐way trade interconnector with a capacity of 5.4 GW and a 12.3 GW import‐only interconnector between GB and other states. The GB–France link transmits electricity from cheaper system to the more expensive one. The total electricity demand in 2030 will be 406 TWh. Gas‐fired power plants w/wo CCS will provide 83 TWh of the total electricity demand, whereas nuclear power plants will produce 74 TWh. In addition, wind farms and solar PVs are expected to deliver ~120 TWh electricity. CHP units will provide 88 TWh electricity in 2030. The electricity traded between GB and France in 2030 was found to be 33 TWh, which is 160% larger compared with 2016. The power import from France is about 27 TWh and occurs in 59% of the time. For 64% of the time, the interconnector with France is fully loaded. The electricity imported via the 12.3 GW interconnector in 2030 is 1 TWh and mainly occurs during winter‐time when the demand in GB is high. De‐rated capacity margin was calculated based on instantaneous electricity demand and varies between −2% and 139%. The impact of the price of the imported electricity via the 12.3 GW link was investigated. Increasing the price of the imported electricity via the 12.3 GW link results in a higher capacity factor for all the generation options except the 12.3 GW interconnector link. [ABSTRACT FROM AUTHOR]

The increased integration of variable renewable generation into the power systems, along with the phase-out of fossil-based power stations, necessitate procuring more flexibility from the demand sectors. The electrification of the residential heat sector is an option to decarbonise the heat sector in the United Kingdom. The inherent flexibility that is available in the residential heat sector, in the form of the thermal inertia of buildings, is expected to play an important role in supporting the critical task of short-term balancing of electricity supply and demand. This paper proposes a method for characterising the locally aggregated flexibility envelope from the electrified residential heat sector, considering the most influential factors including outdoor and indoor temperature, thermal mass and heat loss of dwellings. Applying the method to England and Wales as a case study, demonstrated a significant potential for a temporary reduction of electricity demand for heating even during cold days. For a scenario envisaged a fully electrified residential heat sector in England and Wales, total electricity demand reductions of approximately 25 GW and 85 GW were shown to be achievable for the outdoor temperature of 10 °C and -5 °C, respectively. Improving the energy performance of the housing stock in England and Wales was shown to reduce the magnitude of available flexibility to approximately 18 GW and 60 GW for the outdoor temperature of 10 °C and -5 °C, respectively. This is due to the use of smaller size heat pumps in the more efficient housing stock. However, the impact of the buildings' retrofit on their thermal mass and consequently on the duration of the flexibility provision is uncertain. • A methodology is proposed to characterise the thermal parameters of dwelling stock. • Flexibility from thermal mass of buildings in England and Wales is quantified. • Electricity demand reduction of 87 GW is available at outdoor temperature of -5 °C. • Impacts of buildings retrofits on the amount of flexibility are assessed. [ABSTRACT FROM AUTHOR]

Abstract The increasing decarbonisation of the power and heat sectors in Great Britain poses numerous uncertainties about the future of the gas network. An optimisation model was developed for investigating the operation of future low carbon electricity, gas and heat supply systems. The model was employed to quantify the impacts on the operation of the gas network in Great Britain of transitioning to low carbon power and heat. The modelling results show that the decarbonisation of the power and heat sectors affects the operation of the high and low pressure gas networks differently. A highly electrified heat sector, only slightly changes the gas load duration curve for the high pressure gas transmission network, but significantly affects the load duration curve for low pressure gas distribution networks. In addition, in a future energy system with a large capacity of variable wind and solar generation, and highly electrified heat supply, although the annual volume of gas supply decreases, the peak gas supply during low wind and cold spells remains the same or even exceeds the current figure. This is mainly due to gas-fired power plants operating to their maximum capacity to complement the wind resource and also supply electricity for heat pumps. Highlights • The decarbonisation of heat in GB affect the future role of the gas network. • The operation of an integrated electricity, gas and heat system was investigated. • Electrification of heat in GB reduces the annual gas demand but increases its peak. [ABSTRACT FROM AUTHOR]

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators. [ABSTRACT FROM AUTHOR]

Active demand side response (DSR) will provide a significant opportunity to enhance the power system flexibility in the Great Britain (GB). Although electricity peak shaving has a clear reduction on required investments in the power system, the benefits on the gas supply network have not been examined. Using a Combined Gas and Electricity Networks expansion model (CGEN+), the impact of DSR on the electricity and gas supply systems in GB was investigated for the time horizon from 2010 to 2050s. The results showed a significant reduction in the capacity of new gas-fired power plants, caused by electricity peak shaving. The reduction of gas-fired power plants achieved through DSR consequently reduced the requirements for gas import capacity up to 90 million cubic meter per day by 2050. The cost savings resulted from the deployment of DSR over a 50-year time horizon from 2010 was estimated to be around £60 billion for the GB power system. Although, the cost saving achieved in the gas network was not significant, it was shown that the DSR will have a crucial role to play in the improvement of security of gas supply. [ABSTRACT FROM AUTHOR]

Integration of a large capacity of wind generation in the Great Britain (GB) electricity network is expected to pose a number of operational challenges. The variable nature of wind generation necessitates introduction of technologies that can provide flexibility to generation portfolios and therefore compensate for intermittency of wind generation. In this paper, the efficacy of three options to address electricity balancing challenges was evaluated: flexible gas-fired plants, electricity storage and Power-to-Gas system. The combined gas and electricity network model (CGEN) was enhanced and through adopting a rolling optimisation approach the model aims at minimising the operational cost of an integrated gas and electricity networks that represents a GB system in 2030. The potential impacts of employing each of the flexibility options on the operation of the integrated electricity and gas networks were investigated. The analysis showed that amongst all the flexibility options, the deployment of grid-scale electricity storage will achieve the highest reduction in the operational cost of the integrated system (£12 million reduction in a typical winter week, and £3 million reduction in a typical summer week). The results of this study provide insights on the system-wide benefits offered by each of the flexibility options and role of the gas network in the energy system with large capacity of wind generation. [ABSTRACT FROM AUTHOR]

• A school with PV and battery storage used as a local energy system case study. • Revenue stacking in wholesale day-ahead energy and frequency response markets. • Economic analysis of operating cost and investment viability of battery storage. • Frequency response participation increased revenue and reduced total operating cost. • Stacking frequency response reduced degradation, increasing battery lifetime. 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. [ABSTRACT FROM AUTHOR]

The reliance of Great Britain power generation on the gas network makes it critical to consider the future availability and cost of gas in planning the expansion of the power system. A combined gas and electricity network planning model was used to investigate impacts of various low carbon strategies on regional expansion of the Great Britain gas network out to the 2050s. A number of long term energy supply and demand strategies covering a range of plausible investment policies for Great Britain gas and electricity systems were explored. Reliance of Great Britain on gas imports was projected to vary from 84%, in an energy system with significant electrification of heat and transport sectors and large capacity of nuclear generation, to 94% in a business as usual case. Extensive investment in Liquefied Natural Gas import facilities at Milford Haven and the Isle of Grain was shown to compensate for reduction of indigenous gas supplies. Exploitation of shale gas in north England was shown to reduce the gas dependency of Great Britain in the business as usual case to 74%. Electrification of the heat and transport sectors combined with exploitation of shale gas in Great Britain could reduce import dependency to below 10% by 2050. [ABSTRACT FROM AUTHOR]

Power-to-gas (PtG) converts electricity into hydrogen using the electrolysis process and uses the gas grid for the storage and transport of hydrogen. Hydrogen is injected into a gas network in a quantity and quality compatible with the gas safety regulations and thereby transported as a mixture of hydrogen and natural gas to demand centres. Once integrated into the electricity network, PtG systems can provide flexibility to the power system and absorb excess electricity from renewables to produce hydrogen. Injection of hydrogen into the gas network reduces gas volumes supplied from terminals. In order to investigate this concept, hydrogen electrolysers were included as a technology option within an operational optimisation model of the Great Britain (GB) combined gas and electricity network (CGEN). The model was used to determine the minimum cost of meeting the electricity and gas demand in a typical low and high electricity demand day in GB, in the presence of a significant capacity of wind generation. The value of employing power-to-gas systems in the gas and electricity supply system was investigated given different allowable levels of hydrogen injections. The results showed that producing hydrogen from electricity is capable of reducing wind curtailment in a high wind case and decreasing the overall cost of operating the GB gas and electricity network. The northern part of GB was identified as a suitable region to develop hydrogen electrolysis and injection facilities due to its vicinity to a significant capacity of wind generation, as well as the existence of gas network headroom capacity, which is expected to increase as a result of depletion of UK domestic gas resources. [ABSTRACT FROM AUTHOR]

Abstract: A large amount of wind power generation capacity is expected to be installed in Great Britain by 2020. A considerable amount of this capacity will be located in Scotland. This will require reinforcement of power transmission capacity between Scotland and England. Analysis of the impact of a large amount of wind generation capacity on the electricity network requires modelling of wind power output. Due to the lack of available wind power output data a method for artificially generating wind power output from real wind speed data was developed. The technique is based on monthly wind speed averages used to generate Weibull distributions. Hourly wind power output time series over one year are generated for 18 onshore and offshore locations in Great Britain. Capacity factors, maxima, minima and hourly output variations, their means and standard deviations are calculated. A comparison of statistical parameters of the generated data with those of real wind power output shows that both have similar statistical properties. The geographically distributed wind power output data is used in conjunction with an optimal load flow model for analysing three network case studies. One case study uses a hypothesised 2020 Great Britain transmission network the other two cases enhance the network with an Eastern and Western HVDC line linking Scotland and England. The simulation results illustrate the effectiveness of both HVDC lines in reducing wind generation curtailments and operational costs. [Copyright &y& Elsevier]

• Price arbitrage operation of distributed battery storage is examined. • Local energy system and national power system interactions are considered. • Reformulated bilevel optimisation as a single level mixed integer linear problem. • Comparison of centralised and bilevel optimisations of distributed battery storage. • Influence of retail contracts on value of distributed battery storage investigated. Battery storage provides flexibility to the power system and supports the increased integration of renewable energy sources. Distributed battery storage systems that are behind the meter are operated by their local owners, whose objectives may not align with those of the national power system. This paper presents a Bilevel optimisation approach to investigate the exchange of electricity between distributed battery storage and the national power system. The independent operating objectives of the battery storage systems are explicitly considered to assess their impact on the operation of the national power system. A comparison with a Centralised optimisation approach, that assumes a single objective function for the whole system, shows that the Bilevel optimisation approach captures the independencies of distributed battery storage objectives, while accounting for its interactions with the wider power system. The results show that the Centralised optimisation approach tends to overestimate the value of distributed battery storage for the power system. The results also highlight the influence of the retail contract structure in maximising the value of distributed battery storage for the national power system. [ABSTRACT FROM AUTHOR]

Abstract: In order to achieve a hydrogen economy, developing widespread hydrogen supply systems are vitally important. A large number of technological options exist and are still in development for hydrogen production, storage, distribution…, which cause various pathways for supplying hydrogen. Besides the technical factors, there are other effective parameters such as cost, operability, reliability, environmental impacts, safety and social implications that should be considered when assessing the different pathways as optimal and viable long-term alternatives. To aid this decision-making process, we have developed a generic optimization-based model for the long-range energy planning and design of future hydrogen supply systems. By applying Linear Dynamic Programming techniques, the model is capable of identifying optimal investment strategies and integrated supply system configurations from the many alternatives. Also, the environmental impacts of hydrogen supply system can be evaluated through scenario analysis. The features and capabilities of the model are illustrated through application to Iran as a case study. [Copyright &y& Elsevier]

Abstract: In recent years there have been many efforts to develop a hydrogen energy system in Iran. For instance, the Iranian fuel cell steering committee conducted a project and determined some targets to implement hydrogen in different sectors, especially transportation. In this paper, the costs of building stations and the levelized cost of hydrogen for two types of stations, SMR and electrolysis, with various sizes and capacity factors have been investigated. Also, in the case of electrolysis, hydrogen cost sensitivity to the price of electricity has been examined. According to the cost analysis conducted here, it is understandable that hydrogen costs ($/kg) vary considerably based on station type, size, and capacity factor. Hydrogen production from natural gas results in lower costs ($3–7/kg H2) in comparison to electrolysis ($6–10/kg H2). Hydrogen cost would decrease by increasing the capacity factor, but in the case of electrolysis, from a specific capacity factor, hydrogen cost begins to rise; this is due to the trade off between the capacity factor and the price of electricity in various load zones. In general, hydrogen costs in Iran are estimated from 3 to 10.5$/kg, however a strategy for producing even lower cost hydrogen in the early years of hydrogen implementation, is proposed, which all together indicates that hydrogen could be an economical alternative fuel to substitute gasoline in the Iran''s transportation sector. [Copyright &y& Elsevier]

The evolution of district heating systems is characterized by a trend towards lower supply temperatures and higher energy efficiencies. The most recent advancement in this field is the fifth-generation district heating and cooling systems (5GDHCS). Current literature identifies three key features of 5GDHCS: operating at near-ambient temperatures, the use of distributed booster heat pumps at end-users, and the capability to provide both heating and cooling services. However, there is ongoing debate on whether 5GDHCS represents a significant technological leap beyond its predecessor, the fourth-generation district heating system (4GDHS). To resolve fundamental questions surrounding the 5GDHCS concept and to highlight its distinctive features, this paper conducts a detailed comparison between 5GDHCS and 4GDHS, examining their functional, technological, and operational differences. Based on this, limitations in existing definitions of 5GDHCS are identified, and a refined definition better characterizing its unique attributes and advancement is suggested. Additionally, considering the 5GDHCS-related studies are growing fast, with over 30 works published each year since 2021, this paper tries to complement previous reviews with an updated perspective on its latest research progress and practical deployment, through a comprehensive state-of-the-art analysis. Results show that while 5GDHCS is a promising technology to decarbonize domestic heating and to promote sector coupling between electric power and district heating systems, it is still in the early stage of development, marked by substantial research gaps in technical, policy, regulatory, and market aspects. Analysis on an exhaustive collection of real-life cases also indicates a long way ahead to achieve its widespread commercialization. • Diverged opinions on the term of "fifth-generation" are summarized and discussed. • A comprehensive comparison between 5GDHCS and 4GDHS is provided. • Distinctive features of 5GDHCS are analyzed and a refined definition is suggested. • An updated review on the research progress and practical development of 5GDHCS. • A collection and analysis of research projects and real-life systems are presented. [ABSTRACT FROM AUTHOR]

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions. [ABSTRACT FROM AUTHOR]

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. [Display omitted] [ABSTRACT FROM AUTHOR]

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. • A method was proposed for estimating heat demand of domestic building. • A method was used to investigate cost-effective heat supply options for local areas. • The methods are demonstrated on Neath Port Talbot but can be applied elsewhere. • District heating and air-source heat pump play major role in decarbonising heat. [ABSTRACT FROM AUTHOR]

The electrification of heat supply is a widely discussed strategy for decarbonizing the heat sector in the Great Britain (GB). This impacts the electricity load duration curve and affects the optimal mix of power generation technologies. Additionally, the price of electricity and the emission from the grid determine whether the electrified heat is cost-effective and low carbon. These interdependencies necessitate adopting an integrated approach for long term planning of heat and electricity supplies to ensure cost-effective decarbonization. In this work, we have developed an iterative approach for investigating optimal mix of technologies in electricity and heat sectors considering the interactions between these sectors. This approach was applied to GB as a case study. Firstly, the capacity and operation of various technologies for electricity generation were determined to supply electricity demand (including electricity demand for heating) with a minimum annualized cost. Then, using the levelized cost of electricity calculated in the power generation mix optimization problem, the optimal heat supply mix was determined through the minimization of annualized cost. The iterative optimization of electricity and heat was continued until an equilibrium was achieved. The results were compared with a centralized optimization model that heat and electricity supply problems are solved simultaneously. • Interaction of the heat and electricity supply systems is modeled and investigated. • A bilevel model is developed and solved using an interactive method. • Heat and electricity supply systems are also solved using a centralized model. • Comparison of two methods show the advantage of bilevel modeling of energy sectors. [ABSTRACT FROM AUTHOR]

[Display omitted] • A whole system approach to studying a low-temperature HGSHP system. • 3D coupled TH model with realistic boundaries and dynamic heat pump COP. • Design of a HGSHP system to support a 5th generation DHC network. • Different factors on performance of the HGSHP system and ground thermal behaviour. • Ground moisture transfer, cooling process and buried depth are important. Exploitation of shallow ground and its low-grade heat potential is fundamental to designing 5th generation district heating and cooling (DHC) networks. Horizontal ground-source heat pump (HGSHP) systems are a common way to utilize shallow geothermal energy. Realistic estimation and prediction of performance of a HGSHP system and shallow ground thermal behaviour should consider the whole system including building heating and cooling load, heat pump and ground heat exchanger, and the ground. This should be accompanied by realistic atmospheric and ground conditions. In this paper, a three-dimensional coupled thermal–hydraulic model with realistic boundary conditions adopting a whole system approach is presented. Dynamic heat pump coefficient of performance (COP) that depends on seasonal variation of heating/cooling demand and ground conditions are also considered. Model validations are conducted against experimental and analytical results in literatures. The model is applied for evaluating a HGSHP system to support development of a 5th generation DHC network on a potential site in the UK. Several influencing factors, such as ground moisture transfer, building thermal load mode, buried depth of ground loops, and initial ground temperature profile are studied to assess performance efficiency of the HGSHP system and evolution of ground thermal behaviour in response to heat extraction or rejection into the ground. The results show that 5% of the monthly total heat demand of the site could be met by the designed HGSHP system, consisting of 200 U-shaped ground loops buried at the depth of 3 m and pure water as the heat carrier. Overlooking the ground moisture transfer or hyperbolizing the ground saturation would overestimate the load-carrying capacity of the HGSHP system. The HGSHP system is more efficient with a higher heat pump COP under the heating and cooling mode than under the heating-only mode. Predicted performance of the HGSHP system improves with buried depth of the ground loops. The results also show that a 1 ℃ increase in the undisturbed ground temperature could suffice up to 8% of the monthly total heat demand of the site. [ABSTRACT FROM AUTHOR]

The electricity system balancing is becoming increasingly challenging due to the integration of Renewable Energy Sources (RES). At the same time, the dependency of electricity network on gas supply system is expected to increase, as a result of employing flexible gas generators to support the electricity system balancing. Therefore the capability of the gas supply system to deliver gas to generators under a range of supply and demand scenarios is of a great importance. As potential solutions to improve security of gas and electricity supply, this paper investigates benefits of employing flexible multi-directional compressor stations as well as adopting a fully integrated approach to operate gas and electricity networks. A set of case studies for a GB gas and electricity networks in 2030 have been defined to quantify the value of an integrated operation paradigm versus sequential operation of gas and electricity networks. The results indicate there are significant overall system benefits (up to 65% in extreme cases) to be gained from integrated optimization of gas and electricity systems, emphasizing the important role of gas network infrastructure flexibility in efficiently accommodating the expected expansion of intermittent RES in future power systems. [ABSTRACT FROM AUTHOR]

The integration of large capacities of wind and solar generation into the France and Great Britain (GB) power systems is expected to pose significant operational challenges. Electricity interconnectors can play a role in facilitating the integration of renewable generation in neighbouring countries by allowing power to flow freely between power systems and therefore smooth the net electricity demand. In this paper, role of the electricity interconnectors in efficient balancing of supply and demand in the France and GB power systems was evaluated in terms of overall reduction in the operational costs and curtailment of renewable generation, and also its impact on operation of gas-fired plants. The value of the France-GB interconnectors was studied for two generation mix scenarios in 2030 using PLEXOS. The outputs of the modelling showed the interconnectors will result in larger amount of wind and solar to be absorbed by both power systems which consequently will reduce overall operational costs and CO 2 emissions. In addition, the interconnectors will reduce burden on gas-fired plants compensating for variation in wind and solar generation. This can have a significant value in operation and required investment of gas networks in both countries. [ABSTRACT FROM AUTHOR]

Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced. • The use of blockchain and artificial intelligence for facilitating prosumers to be integrated into smart grids and decarbonising the power systems. • Review how policy designs price of carbon emissions to facilitate the integration of prosumers with renewable energy sources. • Future energy markets with blockchain technologies. • How to apply artificial intelligence technologies for enhancing the control and decision-making in smart grids. [ABSTRACT FROM AUTHOR]

• Emissions may not be reduced under current government guidelines. • Efforts must be made to increase renewables building rates. • Fossil fuels will continue to be important to ensure demand attendance. The use of renewable energy sources in electricity systems has become an important strategy to reduce greenhouse gas emissions in the power sector and to avoid depletion of natural resources. However, uncertainties on renewable power generation, diurnal and seasonal variability, technology costs and regional resources availability are factors that have challenged their large-scale adoption. In this context, studies have been focused on how to best integrate different generation technologies with regards to costs and security of supply. Taking the Brazilian electricity system expansion as a case study, this paper proposes a new hierarchical modelling framework that integrates two electricity planning optimization models: (i) a portfolio theory-based model that deals with the trade-off between security of supply and expected supply cost and (ii) a deterministic least cost model that finds the amount of electricity to be produced by each generation technology. The proposed approach can provide information regarding expected total supply cost, electricity deficit risks, capacity expansion and expected greenhouse gas emissions. Additionally, current government guidelines for renewables expansion are discussed with respect to the Brazilian targets for emissions reduction. Results point to a slight increase in renewables share from 82.4% in 2020 to 85.3% in 2035; however maximum established building rates for renewable generation projects are shown to be very tight and may hamper the achievement of emissions reduction. Overall, it is shown that renewable technologies are attractive for both the environmental and economic spheres, but fossil fuels will continue to play an important role for meeting demand. [ABSTRACT FROM AUTHOR]

Abstract: A combined gas and electricity network expansion planning model was developed. Gas-fired generation plants were considered as linkages between the two networks. The model simultaneously minimises gas and electricity operational and network expansion costs. Additionally it optimally places planned power generation plants around the electricity network. Network expansion was implemented by adding new assets such as pipes, compressors, and storage facilities in the gas network and increasing transmission line capacity in the electricity network. The developed model was used to analyse the GB gas and electricity infrastructure expansion requirements to achieve a low carbon energy system. Two scenarios were implemented, a reference and a low carbon scenario. For both scenarios, CGEN defined a network at lowest cost capable of meeting varying demand and power generation capacity profiles. Greater peak gas demand of approximately 25mcm/d by 2030 in the reference scenario resulted in CGEN adding an additional 1Bcm of gas storage capacity compared with the low carbon scenario. LNG gas supplies were shown to account for over 70% of total gas supplies by the end of the time horizon for both scenarios. The combined gas and electricity network planning approach allows analysis into the interactions between these two networks. This interaction allows variables such as total gas supply (gas used for electricity production is an endogenous variable), which 13% higher in the reference scenario and geographic location of gas fired generation to explicitly take account of the impact on both gas and electricity infrastructures. [Copyright &y& Elsevier]

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. • A novel framework for predicting the real-time reliability of supply in IESs. • Prediction Intervals of variables are used in IES's reliability assessment. • A data-driven model is proposed to improve the computational burden. • Multi-modal system functional states are used to analyse the system' functionality. [ABSTRACT FROM AUTHOR]

Compressor stations play a crucial role in natural gas networks to maintain required pressure levels for transporting gas. Centrifugal compressors commonly used in high pressure gas transmission networks could be driven by gas turbines or electric motors. Including compressor units powered via different fuels in a compressor station allows switching between fuels required by compressors to achieve a set outlet pressure and flow throughput. In this paper, an optimisation model of gas network was developed considering reasonably detailed representation of a compressor station to investigate flexibility provision from the compressor station to the power system. The model was formulated as a mixed integer linear programming (MILP) problem by linearising the nonlinear equations governing gas flow along pipes and compressor power consumption. The model was tested on the gas transmission system in South Wales, UK. The operation of compressors was optimised in response to gas and electricity price subject to meeting operational limits of the gas network. The results showed that the compressors can provide flexibility to the power system through shifting their electricity energy consumption in time or switching between gas- and electric-driven compressor units. It was found that the allowable range for variation of linepack plays a key role in the magnitude and duration of flexibility provision from compressor units. • An optimisation model was proposed for operation of compressors in the gas networks. • Flexibility from coordinated operation of gas- and electric-driven compressor units were quantified. • The value of linepack in providing flexibility to power systems was investigated. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of water and emission limitations on expansion planning of thermal power plants (TPPs), in Iran. TPPs need considerable amount of water for their cooling system. In Iran, water crisis of recent years has made it impossible to operate existing steam power plants in some areas, during summer season. This paper considers water availability and cost in generation expansion planning (GEP) of Iran. There are different types of water resources such as rivers, dams, treated wastewater, seawater, seawater transmission line, saline groundwater, and groundwater. Considering available capacity of water resources in each area and sorting their extraction cost in ascending order, some water consumption cost curves are derived. For this purpose, data of about 200 water resources located in different regions of Iran are analyzed. Provided GEP model includes a 15-years planning horizon and analyzes several scenarios of water availability. Although, in low water price scenarios, air temperature and height above sea level have more impact on placement of TPPs, it is different for high water price scenarios. Also, the effect of emission-related policies on GEP decisions is analyzed by considering various fuel types of TPPs, penalties for emissions, and global restrictions on greenhouse gas production. • Proposes an optimization model for generation expansion planning (GEP) that considers environmental constraints. • Analyzes the availability and price of different water resources in Iran and their effects on GEP decisions. • Examines the impact of temperature and height above sea level, on the siting of thermal power plants in Iran. • Considers decarbonization policies in the expansion planning of power plants in Iran. [ABSTRACT FROM AUTHOR]

With the introduction of new technologies, such as waste heat recovery units (WHRU), associated gas utilization, the energy flow coupling relationship is further deepened within the energy system of the offshore oil and gas production platform. Besides, the energy system is closely linked with the oil and gas production system, and a closed-loop relationship between energy flow and material flow can be revealed. Uncertainties of energy supply and production process may lead to system-wide fluctuations, which threaten the stable operation of the platform. Therefore, an optimal planning model of integrated energy system for offshore oil and gas production platform is proposed in this paper. Firstly, a generalized energy and material flow model is proposed, three matrixes are defined based on laws of thermodynamics, including energy matrix, process matrix and feedback matrix. Secondly, the energy-material conversion relationship between the energy system and production system of a typical offshore oil and gas platform is quantitatively described, together with the coupling between the input and output of the two systems. Thirdly, considering the energy-material balance constraints and the uncertainties of production system, a multi-objective stochastic planning model for the offshore integrated energy system is established, which takes economics and environmental protection into consideration. A Monte Carlo simulation-based NSGA-II algorithm is proposed to solve the model. Finally, the validity and feasibility of the proposed methodology are demonstrated through an offshore oil and gas platform in Bohai, China. Compared with the traditional planning method, the total cost and CO2 emissions of the proposed method are reduced by 18.9% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

This work presents an approach to determine the optimal design configuration and circulation temperature for a given Bi-directional Low Temperature Network (BLTN) using scenario analysis. BLTNs are capable of supplying both heating and cooling via a bi-directional network of 'warm' and 'cool' pipes. They feature decentralised network circulation pumps and building integrated heat pumps, permitting low pipe circulation temperatures and thereby enabling low-grade heat to be rejected into the network. As a case study, a BLTN layout was conceived for a small section of a university campus, currently served by a high temperature heating network with gas-based CHP and boiler plant heat sources. The proposed BLTN utilises a horizontal ground source array and considers future estimated cooling loads which must be served by the network, in addition to existing heating loads. After analysing different design configurations at various heating to cooling load ratios, the headline result from this study is that an ambient temperature design, without centralised ground source heat pumps to control the network temperature, represents the most cost effective option. This result, based on the levelised cost of heating and cooling (LCOHC) over a 50 year period, is consistent regardless of the heating to cooling load ratio for the system. Moreover, as cooling loads are increased, the ambient temperature design becomes increasingly cost-effective relative to other options. • A method for simulation of Bi-directional Low Temperature Networks was developed. • Incorporates supply of heating and cooling from a horizontal ground source array. • Economic analysis of selected design configurations was performed. [ABSTRACT FROM AUTHOR]

The reliability analysis of IESs (Integrated Energy Systems) is a complicated task because of the complex characteristics of different subsystems and the multi-scale dynamics that develop therein. To effectively address such problems, this paper proposes a systematic framework to analyse the reliability of energy supply in IESs, considering the dynamics of IESs and the inter-relationships among uncertainties. First, based on the linepack-based performance analysis model of IES, a quasi-steady-state model is established to model the dynamic behaviours in IESs, properly accounting for practical engineering and operational strategies. Then, considering the inter-correlations among different uncertainty sources and time-dependent relationships of each variable, a model that combines the statistical structure of copula with the machine learning method of stacked autoencoder (CSML) is adopted to establish the timely multivariate joint distributions for variables. Monte Carlo simulation combined with Order Statistics is used for assessing supply reliability. Case studies are performed on a realistic IES that combines an IEEE-15 power system with an 18-node natural gas pipeline network. The efficiency and accuracy of the quasi-steady-state model are validated. The reliability evaluation results show that the inter-correlations among variables and time-dependent relationships of each variable have great effects on the system reliability assessment. The consideration of linepack can significantly improve the supply reliability of IES whereas the management strategy of linepack may lead to some risky points. • A unified quasi-steady-state electric-gas IES model. • The correlations among uncertain factors are considered. • The impact of the management strategy of linepack are investigated. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • A novel data-driven prosumer-centric energy scheduling model to improve the computational efficiency and scalability. • Convolutional neural networks map pricing patterns to potential scheduling decisions. • Real-time scenarios selection to adapt uncertain scenarios with dynamic operations of prosumers. • Case studies validate the efficiency under various IEEE test distribution systems and uncertain scenarios. [ABSTRACT FROM AUTHOR]

• Proposing optimization model for gas and power systems. • Incorporating resiliency analysis algorithm into the integrated operation of gas and power systems. • Proposing a novel possibilistic approach to deal with uncertainty. • Investigating the role of demand response and electrical vehicles. Gas-fired power plants are commonly employed to deal with the intermittency of renewable energy resources due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas-fired power plants, which makes the operation of these systems even more interdependent. This study proposes a novel possibilistic model for the integrated operation of gas and power systems in the presence of electric vehicles and demand response. The model takes into account uncertainty in demand prediction and output power of wind farms, which is based on possibility and necessity theories in fuzzy logic through modeling uncertain parameters by Gaussian membership function. Moreover, a contingency analysis algorithm based on maximin optimization is developed to enhance the resiliency in the integrated operation of these systems by finding the worst-case scenario for the outage of components. The proposed model is implemented on a Belgium gas network and IEEE 24-bus electricity network. It is demonstrated that the possibilistic model allows the gas network to respond to the demand variations by providing a sufficient level of linepack within the pipelines. As a result, gas-fired power plants are supposed to commit more efficiently to cope with the intermittency of wind farms, which reduce the wind curtailment by 26%. Furthermore, it is quantified that through applying the contingency analysis algorithm in presence of demand response and electrical vehicles, the costs of operation and load shedding is reduced up to 17% and 83%, respectively. [ABSTRACT FROM AUTHOR]