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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.

Seismic events and aftershocks can cause devastating impacts on overground and underground energy system infrastructure. This article designs a novel approach to restore energy system functionality in response to seismic stresses by using coordinated dynamic system reconfiguration and operation. First, the impact of seismic stresses on system infrastructure is evaluated, considering critical system components, including electricity lines, substations, gas pipes, and operable units including combined heat pump (CHP), electrolyser, soft open point (SOP), energy storage units. Load curtailment is assumed to be caused by system branch overloading, reversed gas flows, and isolated branches. The objective of the proposed operation method is to maximize overall system restoration capability. Responding to seismic stresses and aftershocks, storage, SOPs and coupling components are dynamically operated to restore both gas and electricity supply thus reducing losses. A service index is quantified by considering both load curtailment and recovery time. A representative integrated electricity and gas system is employed to demonstrate the effectiveness of the proposed method. Results illustrate that the adverse impact of seismic stresses can be effectively reduced with the proposed model. This work provides system operators a powerful tool to restore the functionality of energy systems under seismic events, helping maintain supply security.

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems.

Volt/VAR optimization (VVO) is one important operation in distribution systems to maintain acceptable voltage profiles. However, the high penetration of renewable generation poses severe challenges to VVO, leading to voltage deviation and fluctuation. This is further complicated by the growing coupling between the electricity and natural gas systems. To resolve the unacceptable voltage deviation under energy system interdependency, this article proposes a cooptimization of VVO for an integrated electricity and gas system (IEGS) with uncertain renewable generation. A two-stage data-driven distributionally robust optimization is developed to model the coordinated optimization problem, which determines the two-stage VVO and operation schemes with dispatch and corrective adjustment through active power regulation and reactive power support in both day-ahead and real-time stage. A semidefinite programming is reformulated to ensure the tractability and the proposed problem is solved by a constraint generation framework. Simulation studies are conducted on a 33-bus-6-node IEGS. Case studies demonstrate that the interdependency between electricity and gas systems reduces the significant operation cost and voltage rise. It, thus, can benefit integrated system operators with a powerful operation tool to manage the systems with fewer costs but integrate more renewable energy while maintaining the high supply quality.

Volt-VAR optimization (VVO) has been investigated extensively in power systems. However, under the era of integrated energy systems (IES), the growing interdependencies between different energy systems complicate traditional VVO. This is further hardened by incurred gas quality problems due to the hydrogen injection in IES, produced by widely applied power-to-gas (P2G) facilitates that couple between power and gas systems. This paper develops a two-stage volt-VAR-pressure optimization (VVPO) model for PV-penetrated IES to manage the variation of system voltages while managing gas quality indices. In addition to the traditional voltage regulating devices, P2G facilities, which can mitigate fluctuating PV output via converting the surplus generation into hydrogen, are also used for voltage management. A two-stage distributionally robust optimization (DRO) based on moment information is utilized to model PV uncertainty. A semidefinite programming model is formulated and finally solved by the constraint generation algorithm. A 33-bus-20-node IES is used to verify the effectiveness of the proposed VVPO on voltage management, ensured gas quality with high economic efficiency. The proposed VVPO is applicable to injecting other green gases into gas systems while ensuring power quality and enable system operators to provide low-cost but high-quality multi-energy to customers.

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills.

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity-transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi-sequential Monte Carlo simulation is presented. This study proposes a novel reliability-oriented multi-objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large-scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method.

Combined borehole (BH) heat storage systems, batteries and power-to-gas system have the potential to shift load, reduce carbon emissions, provide hydrogen for fuel cell cars and save energy costs for end customers on an extended scale. This study proposes an optimal operation strategy for a local multi-vector energy storage system, which includes batteries, BH thermal storage, the power to the gas system and the fuel cell cars system. These storage systems can be divided into the short-term storage system and inter-seasonal storage system or low capacity storage system and high capacity storage system. The optimisation problem is divided into a two-stage framework, (i) the first stage optimisation is seasonal optimisation, which gives an approximate optimal operation plan for BH heat storage systems in the following year; (ii) the second stage develops a day-ahead robust optimal plan for all storage systems. Finally, the algorithm will return to seasonal optimisation to update the operation plan for BH heat storage systems to make results more accurate. The test case of eight nodes illustrates that the combined energy system of photovoltaic, heat pump power to gas, BH and batteries can provide hydrogen to fuel cell cars and significantly save power costs for customers with the optimal operation.

The large-scale penetration of wind generation imposes challenges on the security of power system operation due to the intermittency and stochastic volatility. Hybrid energy storage system (HESS), which combines battery banks and super-capacitors, is applied in this study to smooth wind fluctuations to facilitate the grid-friendly integration. To optimally schedule HESS charge/discharge in an online receding horizon, a novel two-stage model predictive control (MPC) scheme is proposed. The first stage determines the initial charge/discharge profiles for battery banks at a large time scale, while the second stage quantifies the optimal amendment to update them and determines the operation strategies for super-capacitors at a smaller time scale. According to the rolling update of wind forecasting, the two-stage model is successively solved in a receding horizon to generate the most appropriate operation strategies. The proposed method also optimises the state of charge of HESS, yielding sufficient margin to cope with wind uncertainties, making HESS operation more reliable and robust. The case study demonstrates that the proposed model can enable an effective smoothing effect on wind generation volatility by fully utilising energy storage systems of various distinct characteristics, providing a powerful tool to facilitate the smooth integration in a large scale in practice.

Power system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under an ineffective operation. This article applies energy storage (ES) to reduce system peak and the congestion by the robust optimization, considering the uncertainties from the ES state-of-charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for the ES, as a benchmark, is designed to reduce the variance of the branch power flow based on the least-squares concept. Then, a robust model is built to optimize the ES operation with the uncertainties in the severest case from the load, renewable energy, and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram–Charlier expansion methods. The ES SoC uncertainty is modeled as an interval uncertainty set in the robust model, solved by the duality theory. These models are demonstrated on a grid supply point to illustrate the effectiveness of a congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing the system congestion. This robust optimization-based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering the large-scale network investment.

Consumers in the united kingdom typically spend, on average, p33 billion/year for electricity, of which a significant portion (21% in 2017) is paid to distribution companies for delivering electricity to customer premises. To protect consumers' interest, Ofgem (Office of Gas and Electricity Markets), the energy regulator in the United Kingdom, is actively reforming network charges so that they remain usable for the purpose of facilitating the industry?s transition to lowcarbon emissions.

The European power grid is one of the largest regional interconnected power grids in the world. It realizes a multinational grid operation, which is rare. The total installed capacity of the European power grid is the largest throughout the world. In addition, the integration and utilization of renewable energy in this grid is a great benchmark for other countries and can help promote energy transformation and achieve a high proportion of renewable energy consumption. Based on the analysis of the existing status of the European interconnected power grid and the development history of this power grid, this paper summarizes four key development stages of the European power grid. In addition, the characteristics of each stage and the development prospect of the European power grid are analyzed. On this basis, this paper gives suggestions for the development and construction of China’s energy internet; this can provide valuable reference for further studies on China’s energy internet.

Recent developments in fuel cell (FC) and battery energy storage technologies bring a promising perspective for improving the economy and endurance of electric aircraft. However, aircraft power system configuration and power distribution strategies should be reasonably designed to enable this benefit. This paper is the first attempt to investigate the optimal energy storage system sizing and power distribution strategies for electric aircraft with hybrid FC and battery propulsion systems. First, a novel integrated energy management and parameter sizing (IEMPS) framework is established to co-design aircraft hardware and control algorithms. Under the IEMPS framework, a new real-time power distribution algorithm with a flexible ratio is established to facilitate integrated parameter optimization, which can adapt to different power system configurations. Based on the comprehensive analysis of hydrogen economy, FC aging cost, and aircraft stability, a multi-objective parameter optimization model is established to decide the size of aircraft energy storage systems and hyper-parameters in the power controller. The X-57 Maxwell, an experimental electric aircraft designed by NASA, is employed to verify the developed methods. This work provides a novel power system configuration, sizing, and power management method for future commercial aircraft design, and it can further promote the aviation electrification process.

In recent years, the idea of green aviation and environmental protection has received increasing attention from the aviation industry. Hydrogen energy has an important role in the transition to low-carbon energy systems. To address that, this paper conducts the techno-economic analysis for the hydrogen energy system, photovoltaic energy (PV, battery storage system (BSS, electric auxiliary power unit (APU of aircraft, and electric vehicles (EVs into the electrified airport energy system. Specifically, the model quantifies aircraft electrical load based on passenger travel behavior, establishes a corresponding APU load characteristic model, and establishes an EV charging load profile based on the flight schedule and sequencing algorithm. A mixed-integer linear programming (MILP optimization method based on life cycle theory was proposed, to minimize the total costs of hydrogen integrated energy systems for airports (HIES . However, the resilience advantages of hydrogen energy concerning power failure are little explored in existing academic research. Thus, a resilience assessment method and improvement measure were proposed for HIES. Case studies have been conducted under different optimal hydrogen energy integration configurations and disaster times with resilience assessment by considering periods when the power supply capacity of the grid is insufficient. The results show the effectiveness of the proposed method.

It is of significant economic value to use an electric boiler to coordinate the heating network and power grid to reduce wind curtailment during heating supply seasons in the north area. This paper presents an optimal economic dispatching model of combined heat and power energy systems to minimize the total operation cost and wind curtailment by using electric boilers. The proposed model comprehensively considers the thermal characteristics of buildings and the constraints of electric and thermal power balancing, combined heat and power (CHP) units, heat storage tanks, electric boiler power, and wind power. To address this model, a linearization optimization model is built, where different dispatch strategies under CHP only, CHP + HST (heat storage tanks), and CHP + HST + EB (electric boiler) different conditions are conducted. Thereafter, the relationship between indoor temperature and thermal power is analyzed with consideration of the temperature time-delay effect and the heat loss on heating balance. A 6-node combined heat and power energy system is used to validate the effectiveness of the proposed model and solution method. Furthermore, the case studies illustrate the impact of heat storage tanks and the electric boiler on wind power curtailment and coal consumption.

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation.

The wide implementation of information and communication technologies (ICT) cause power system operations exposed to cyber-attacks. Meanwhile, the tendency of integrated multi energy vectors has worsened this issue with multiple energy coupled. This paper proposes a two-stage risk-averse mitigation strategy for water-energy systems (WESs), incorporating power, natural gas and water systems against false data injection attacks (FDIA) under water-energy nexus. The FDIA on individual sub-systems is modelled through hampering false data integrity to the systems. An innovative two-stage risk-averse distributionally robust optimization (RA-DRO) is proposed to mitigate uneconomic operation and provides a coordinated optimal load shedding scheme for the nexus system security. A coherent risk measure, Conditional Value-at-Risk is incorporated into the RA-DRO to model risk. A Benders decomposition method is used to solve the original NP-hard RA-DRO problem. Case studies are demonstrated on a WES under water-energy nexus and results show that the effectiveness of the method to mitigate risks from potential FDIA and renewable uncertainties. This research provides WES operators an economic system operation tool by optimally coordinating energy infrastructures and implementing reasonable load shedding to enhance cybersecurity.

Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method.

International audience; Model order reduction (MOR) has demonstrated its robustness and wide applicability in simulating large-scale mathematical models in the engineering research domain. In this paper, MOR techniques are applied to quantify relevant reliability metrics of power distribution systems and the impact associated with the integration of different smart grid technologies. To the best of the authors' knowledge, this is the first application of MOR techniques of balanced truncation to derive reliability models of electricity networks, which exhibit a reduced number of equivalent components and thus simplify the complexity for network analysis. The extensive case studies presented, based on both radial and meshed systems, demonstrate that the proposed technique allows for a faster reliability assessment through Monte Carlo simulation while preserving high accuracy. The proposed methodology can also be applied to systems endowed with photovoltaic and energy storage technologies, emphasising that this approach represents a promising starting point for reliability analysis of more complex systems, which are normally characterised by a large penetration of these distributed energy resources.

The adjustable capability of distributed energy systems responding to the incentives of the upper energy supply system has been significantly improved by energy storage and renewable energy technologies. Most existing research focuses on evaluating the flexibility of distributed energy system itself or the demand response potential of end-users, but there is no specific model which takes the distributed energy system as an integrated load and evaluates its adjustable capability from the perspective of the upper energy supply system. To fill the research gap, this paper defines the adjustable capability of distributed energy systems, describes its characteristics, and proposes a unified evaluation model. Then, from the perspective of energy demand and supply sides, it quantifies the impact of each influential factor in different energy links on the adjustable capability and studies the interactive mechanism between devices within the system. Thereafter, the adjustable capability of distributed energy systems under typical scenarios at a single moment is evaluated, and the impact of economic constraints on the adjustable capability is also extensively analyzed. Accordingly, this paper proposes a sequential recurrence method to evaluate the adjustable capability of distributed energy systems against three different initial states: unknown initial state, fixed initial state, uncertain initial state. Finally, the adjustable capability concept is demonstrated on a practical industrial park to verify the effectiveness and practicability. This study deepens the connection between distributed energy systems and upper energy supply system in the Energy Internet at the energy information level. Which enables distributed energy system to quantize its energy demand range for the upper energy supply system and realize its own reliable operation and rolling optimization. In addition, this evaluation method allows upper energy supply system to plan, overhaul and dispatch more economically and reliably on the basis of understanding the energy demand of distributed energy system. Moreover, upper energy supply system can formulate the demand response strategy with the maximum revenue by balancing the size of adjustable capability interval of distributed energy system and the investment cost of demand response.

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

Facilitated by advanced information and communication technologies (ICTs), local energy trading develops rapidly, playing an important role in the energy supply chain. Thus, it is essential to develop local trading models and strategies that can benefit participants, not only stimulating local balancing but also promoting renewable penetration. This paper proposes a new local energy trading decision-making model for suppliers by using the Cournot Oligopoly game, considering the uncertainty costs of renewable energy. Four types of representative energy providers are modelled, traditional thermal generation, wind power, photovoltaic (PV) power, and electricity storage. The revenue of these technologies is extensively formulated according to their operation cost, investment cost, and income from selling energy. The uncertainty cost of renewable generation is integrated into the trading, modelled as a penalty for potential energy shortage that is derived from output probability distribution function (PDF). This trading model is formulated as a non-cooperative Cournot oligopoly game to enable energy suppliers to maximize their profits through local trading considering price. The response of the customer to energy price variations, i.e. demand elasticity, is also included in the model. A unique Nash equilibrium (NE) and optimum strategies are derived by the proposed Optimal-Generation-Plan (OGP) Algorithm. As demonstrated in a typical local market, the proposed approach can effectively model and resolve multiple suppliers’ competition in local energy trading. It can work as a vehicle to facilitate the trading between various generation technologies and customers, realising local balancing and benefiting all market participants with enhanced revenue and reduced energy bills.

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wasserstein metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO provides a voltage-regulated economic operation scheme with gas quality ensured that contributes to high-quality but low-cost multi-energy supply to customers. British Council (Grant Number: 515761951); National Natural Science Foundation of China (Grant Number: 72042018 and 71621002).

Motivated by the incentives of policies, renewable generation is flourishing at a high rate to distribution networks. To integrate it effectively and efficiently, local energy trading and local markets have been proposed and applied in practice. With the substantial penetration of renewables in local energy markets, there are severe uncertainties in energy procurement for customers to comprehensively consider. This paper proposes a novel portfolio-theory-based approach for customers to make decisions in procuring energy in local energy markets from various types of providers. A new local market model is designed, which consists of the day-ahead market and real-time market. The portfolio theory is adopted as a tool to help customers to procure energy from multiple suppliers to minimize total energy costs and associated risk simultaneously. The selection of portfolios is fundamentally different regarding their individual risk preferences. Based on the mathematic modeling, portfolio optimization is conducted and the optimal procurement strategies can be generated for consumers under their acceptable risk level. The model has been extensively demonstrated in the case with renewable and traditional energy providers.

Power-to-gas (P2G) can convert excessive renewable energy into hydrogen via electrolysis, which can then be transported by natural gas systems to bypass constrained electricity systems. However, the injection of hydrogen could impact gas security since gas composition fundamentally changes, adversely effecting the combustion, safety and lifespan of appliances. This paper develops a new gas security management scheme for hydrogen injection into natural gas systems produced from excessive wind power. It introduces four gas security indices for the integrated electricity and gas system (IEGS) measuring gas security, considering the coordinated operation of tightly coupled infrastructures. To maintain gas security under an acceptable range, the gas mixture of nitrogen and liquid petroleum gas with hydrogen is adopted to address the gas security violation caused by hydrogen injection. A distributionally robust optimization (DRO) modelled by Kullback-Leibler (KL) divergence-based ambiguity set is applied to flexibly control the robustness to capture wind uncertainty. The KL divergence-based ambiguity set defines uncertainties within a measured space which limits the shape of probability distributions. Case studies illustrate that wind power is maximally utilized and gas mixture is effectively managed, thus improving gas security and performance of IEGS. This work can bring many benefits: i) ensured gas security under hydrogen injection ii) low system operation cost and iii) high renewable energy penetration. It can be easily extended to manage injections of other green gases into IEGS.

With the advanced ICT (information and communication technology) support, the local energy trading rapidly develops and plays an important role in energy supply in recent years. Because the demand and supply directly occurs at local level, the transmission cost saves a lot and the distributed renewables energy resources are capable to be used in energy supply. This paper presents a multi-supplier trading system for the local power market, which is formulated as a non-cooperative Cournot game. In this model, four types of providers: thermal power plant, wind power plant, PV power generation and customer-operated storage electricity supplier are considered separately. Their cost functions have been extensively formulated. A unique Nash equilibrium (NE) exists and the optimum strategies can be derived as demonstrated in a typical local market. The proposed approach can effectively model local energy trading of different types of energy providers.

Severe natural catastrophes may directly cause large-scale cascading failures in energy systems. This paper presents a comprehensive scheme of vulnerability assessment for multi-energy systems, including both electricity and natural gas. Differing from cascading failure theory, this scheme employs PageRank algorithm to measure the structural importance of the integrated system. A modified weighted PageRank methodology is applied to determine the importance of nodes in both natural gas and electricity networks. The effectiveness of this scheme is demonstrated and analyzed via an integrated energy system in the case study. This algorithm can help identify the weakness in the integrate energy system so that purposeful planning and operation strategies can be deployed to increase the security.

A large volume of wind power is curtailed worldwide due to the intermittency and limited transportation capacity of electrical power systems. New technologies, Power-to-Gas via electrolysis, can convert excessive wind power into hydrogen to be transported by natural gas systems. However, the injection of H2 into natural gas pipelines can cause gas quality issues due to changing gas compositions. This paper investigates the impact of injecting H2 converted from wind power on natural gas quality. Two key indexes used to measure gas quality, Wobbe Index and Combustion Potential, are introduced to examine the impact. Then, in order to bring the two indexes into acceptable statutory ranges, H2 is mixed with Liquid Petroleum Gas and Nitrogen. A robust optimization model, considering wind power uncertainties, is thereafter developed to manage the gas mixture, maximize H2 injection. This paper uses the dynamic gas system model to represent real-time pipeline flows, which can better reflect gas flow features over time. The proposed method is demonstrated on a small integrated gas-electricity system. Results illustrate that excessive H2 injection will reduce Wobbe Index but increase Combustion Potential. The robust optimization approach can effectively manage the mixture while ensuring gas quality with an uncertain wind power supply. The proposed method is beneficial to reducing renewable energy curtailment and maximizing H2 injection, benefiting electricity system operators with low operation costs and wind power more penetration.

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers' benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

Renewable energy is the key to meeting increasing electricity demand and the decarburization targets in the generation mix. However, due to constrained power network capacity, a large volume of renewable generation is curtailed particularly from wind power, which is a huge waste of resources. There are typically three approaches to addressing excessive renewable: direct curtailment, the reinforcement of networks to expand transfer capacity, and the conversion of excessive renewable into other energy types, such as hydrogen, to transport. The costs and benefits of the three approaches could vary significantly across location, time, and penetration of renewable energy. This paper conducts a cost-benefit analysis and comparison of the three techniques to address wind curtailment. It uses a reduced 16-busbar UK transmission network to analyze the performance of the three approaches. The UK 2020 generation mix is used to quantify the saved renewable energy and incurred costs. The payback time and net present value of the two investment techniques are compared. From demonstration, it is reasonable to conclude that converting excessive wind power into hydrogen to transport is an environmentally friendly and cost-effective way to address wind curtailment.

This paper proposes a new problem formulation to simplify the mathematical representation for a hybrid ac/dc domestic energy system. Instead of building the formulation from a component level, the new formulation is built at the whole system level such that all power transfers between ac/dc or dc/ac are reflected in the ac power drawn from the main grid. This is achieved by assigning the ac power drawn from the grid as a piecewise function of local dc power, each slope in the function represents the conversion efficiency, making it flexible to consider a wide range of conversion efficiencies for different system components. The new formulation substantially reduces the number of variables/constraints, improves efficiency modelling accuracy and increases the search efficacy. The piecewise functions are directly solved by a mixed integer linear programming (MILP). The performance of the proposed formulation is illustrated by the hybrid ac/dc energy systems at a primary school in the U.K. The results show that the proposed problem formulation and the MILP solution method provide an effective optimal control strategy for a mixed ac/dc domestic energy system in the presence of variable tariffs and differing conversion efficiency, achieved a cost saving of 24% increase in energy bills compared with the traditional approach.

When wind turbine is connected to the power grid, the turbine-grid interactions restrict its lifespan and produce adverse effects to the grid. Electrical or mechanical oscillations caused by turbine-grid interactions could gradually be weakened or amplified seriously at a certain frequency. This article discusses all types of interactions between a wind turbine and the grid and the influence of the initial operating point, the parallel capacitor, and the series capacitor on the stability of system. Firstly, an integrated dynamic electromechanical model of a single fixed-speed induction generator (FSIG) wind generation system is derived based on the small signal model of each element. Then, a method of stability analysis based on Kharitonov theorem is introduced. Then, the model details the frequency and damping ratio of oscillation mode and classifies those modes' interactions according to participation factors, followed by time domain simulations by the single FSIG generation system to testify the results. Finally, the article studies all the oscillation modes when the initial operating point, the parallel capacitor, or the series capacitor change in the integrated model, which can be verified by Kharitonov polynomials. The indicative findings help to design the damping control to mitigate turbine-grid interactions.

The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation.

Energy hubs, which integrate multiple energy vectors through converters, can enhance the value of Integrated Local Energy Systems (ILES) via increased flexibility and reduced costs. However, uncertain renewable energy and the non-convex, non-linear properties of energy flows complicate the modelling and operation of energy hub systems. This paper develops chance-constrained optimization methods for planning and operation of energy hub systems under uncertainty. The non-linear formulations of power and gas flows are relaxed by convexification methods, leading to a formulation of Second Order Cone Problem (SOCP), which can be efficiently solved to global optimality. The correlation between geographically close wind generators connected to the hub systems is modelled by establishing their relation using Gaussian copula. The proposed chance-constrained optimization is demonstrated on a six-hub system within a multi-vector energy distribution network with 7 electrical buses and 7 gas nodes. The value of different levels of system integration through the installation of energy hubs is investigated. The results show that by combining system integration via energy hubs with chance constrained operation, the proposed method can reduce operating costs and increase renewable energy yields, thereby benefitting hub system operators and customers with reduced energy infrastructure investment and energy costs.

This study presents a multi-objective optimization process to determine the optimal operations of combined heat and power (CHP) units and energy hub according to present value and carbon emissions. Mixed integer linear programming (MILP) model is applied to a local energy system including both electricity and gas to achieve multi objectives (energy costs versus carbon emissions). The optimization problem is formulated on the coupling electricity load and gas load with an energy hub and CHP model for optimal design and operation. The components of the energy hub includes solar thermal system, heat pump, battery, photovoltaics (PV), CHPs and thermal storage system. The results show a set of optimal design solutions of each component including their half-hourly operations and trade-off between them to achieve multi-objectives. The total cost including energy cost, carbon emission and investment with energy hub will be 112 million pound lower compared to that without energy hub system in a 20 year time frame.

This paper designs a novel dynamic tariff scheme for demand response (DR) by considering networks costs through balancing the trade-off between network investment costs and congestion costs. The objective is to actively engage customers in network planning and operation for reducing network costs and finally their electricity bills. System congestion costs are quantified according to generation and load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might face the same energy signals but they are differentiated by network cost signals. Once customers conduct DR during system congested periods, the smaller savings from investment and congestion cost are considered as the economic singles for rewarding the response. The innovation is that the method translates network congestion/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. A typical UK distribution network is utilised to illustrate the new approach and results show that derived economic signals can effectively benefit end customers for reducing system congestion costs and deferring required network investment.

Small signal stability of permanent magnet synchronous generator (PMSG)-based wind turbines connected to the power grid should be studied properly in order to facilitate damping strategy design. In this paper, unified small-signal models for different types of PMSGs are developed to study their small-signal stability. The models are composed of mechanical systems, electrical systems and control systems. A two-mass shaft model for the mechanical system is provided to analyze the dynamic and steady-state behaviors of the wind turbine and generator rotor. Meanwhile, PMSG, converter system and transmission line are separately modeled to build unified small-signal models for three PMSG-based wind turbine generator systems (WTGS). Then, based on unified small-signal models, eigenvalue analysis is conducted to determine the relation between different oscillation modes and state variables through calculating participation factors. With modal analysis, the developed small signal models are able to find out all types of oscillation modes for PMSGs connected to the power grid, which are subsynchronous oscillation (SSO), subsynchronous control interaction (SSCI) and low-frequency oscillation, including frequency and damping of each oscillation mode. Different initial values of the small signal models can influence both frequencies and damping ratios of oscillation modes, which lay basis for further damping strategy study.

As large-scale wind farms (WFs) are integrated with the power grid, the interaction between the WF and the grid may excite torsional vibration of the shaft of the wind turbine. To study the shaft's torsional vibration characteristics of a doubly fed induction generator (DFIG)-based WF, a detailed small-signal model of DFIG is established first. Then the small-signal model for a WF composed of multiple DFIGs is developed on the basis of the single-machine model. Modal analysis is employed to investigate the torsional vibration characteristics of a WF made up of several identical DFIGs, whose accuracy is also demonstrated through time-domain simulation. To simplify the torsional vibration analysis for the DFIG-based WF, a reduced-order equivalent model is proposed. The results obtained from modal analysis show that the equivalent model not only precisely maintains all the torsional vibration modes of the original small-signal model but also greatly reduces the computation complexity. With the equivalent model, the ‘curse of dimensionality’ problem is solved in torsional vibration analysis for large WFs, which is of great help in the design of further damping schemes. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The increasing penetration of intermittent, low carbon generation poses a major threat to transmission system frequency stability. To mitigate this issue, future system frequency response can be provided by not only the generation side, such as from large, synchronous generators, but also by the customer side, such as from flexible technologies. This paper develops a novel model to evaluate the contribution from domestic Combined Heat and Power (CHP) plants to system frequency in the GB power system. This is achieved by controlling the electricity production from CHPs in response to significant fluctuations in system frequency. The results show that domestic CHP could increase system frequency stability with minimal adverse impact on the customers.

Torsional vibrations present in the drivetrain excited by turbulent winds or grid disturbances (often manifesting as voltage sags) can produce severe stresses on the components of wind turbines. This study presents a damping method for the rotor side converter of a doubly fed induction generator (DFIG)-based wind turbine to improve system damping during small grid disturbances. Within the damping strategy, torsional damper is included into the power loop of the rotor side converter to provide damping for the system. During vibrations, a well-designed low-pass filter (LPF) is employed by the torsional damper to extract the dynamics of the generator speed and to generate a damping signal. To demonstrate the proposed damping approach, the small-signal stability model of a wind turbine connected to an infinite bus system is presented, and the eigenvalue analysis is conducted to verify the damping effect introduced by the torsional damper. In the case study, grid voltage sag at the point of common coupling (PCC) is applied to excite the torsional vibration of the wind turbine shaft. The validity and effectiveness of the proposed damping approach are testified by using the simulation results against theory analysis.

The power-gas-coupling can realize the cascade utilization of energy in the integrated energy system, which is conducive to improving the utilization of energy and reducing pollution gases emissions. With the installation of smart metering, two-way communication between suppliers and consumers is feasible, which enables the implementation of demand response. A generic optimal planning model is proposed to assess the economic and environmental benefits of the capacity allocation of the grid-connected integrated energy system considering both price-based demand response and incentive-based demand response respectively. The optimal planning problem is formulated as a mixed-integer linear programming model with the objective to minimize the total annual cost. The results from three configuration modes are compared in the case study, which illustrate the economic and environmental benefits from demand response. In addition, the impact of the sales capacity constraint on the grid and the fluctuation of electricity and gas prices on the planning of the integrated energy system are also extensively studied considering demand response.

With the high penetration of distributed renewable resources, local energy trading makes it possible to trade energy from local renewable sources, such as small-scale photovoltaics panels and wind turbines. In this paper, a novel Hotelling Game based local energy trading is proposed in this paper to determine the optimal strategies of the minimum total costs. Both network cost of transportation and energy cost of generation are considered in the trading decision-making. The network cost is determined based on the MW-Mile method to reflect the usage of the network in energy trading. The local trading is modelled as a Hotelling Game and the optimal trading strategies can be obtained for the customer to minimize their energy bill. The proposed energy trading method is demonstrated on a representative system to illustrate the significance of network cost in the local trading scenario.

Energy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers' electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.

As a clean energy, wind power has been extensively exploited in the past few years. However, oscillations in wind turbines, particularly those from controllers, could severely affect the stability of power systems. Therefore, oscillation suppression is a recent research focus. Based on the small-signal model eigenvalues and participation factors, this paper detects the sub-synchronous interactions (SSI) mainly determined by converters' PI parameters in a grid-connected doubly fed induction generator (DFIG). With the aim of oscillation restraint, a novel optimization model with the reference-point based non-dominated sorting genetic algorithm (NSGA-III) and the t-distributed stochastic neighbour embedding (t-SNE) is developed to explore and visualize optimal ranges of PI parameters, facilitating the selection of the appropriate PI parameters to augment the damping. Additionally, to study the adaptability of the optimal PI parameters, interactions performance of the system that uses optimal parameters is studied with different output levels of the wind turbine. Finally, a time domain simulation and a practical experiment are conducted to demonstrate the effectiveness of the proposed approach. Results illustrate that the SSI of a grid-connected DFIG is suppressed by the optimization model. This study is highly beneficial to power system operators in integrating wind power and maintaining system stability.

This paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges.

The flexibility evaluation of distribution networks has attracted significant research attention with the increasing penetration of renewable energy. One particular gap in existing studies is that little attention has been paid to the probabilistic characteristics of uncertain regions. In this study, a novel sequential flexibility evaluation method is proposed based on the feasibility analysis of the uncertain region of photovoltaic active power and load demand. The model features the uncertain region with probabilistic characteristics, which is essential for analysing the impact of probabilistic characteristics of uncertain variables (PCUVs) on flexibility evaluation. The sequential direction matrix is adopted to reflect the major factor of flexibility shortage. The evaluation procedure is modelled as a bi-level optimisation problem. Demonstrated by the simulation results, the flexibility index is larger by considering the PCUV. Furthermore, the elements in the sequential direction matrix indicate that the photovoltaic power during midday is the major cause of flexibility shortage.

With the development of distributed energy resources (DERs) and the increasing flexibility in active distribution networks (ADNs), the economic operation of ADN coordinating both generation and demand sides is required to be studied. In this paper, considering the electricity price response of DER, a coordinated operation strategy for ADN is presented based on a bi-level agent framework. The DER agent makes their own response based on the technical operability and economic consideration, while the ADN agent will finally coordinate each participant by using the interactive benefit prioritization (IBP) principle. The simulation results indicate that the proposed strategy cannot only reduce the power imbalance but also improve the economic operation of ADN. Moreover, consumption of renewable energy is ameliorated as well.

Similar to the electrification of the automotive industry, the growing concerns with the shortage of fossil fuels has also called for a paradigm shift in aviation industry. To promote the aviation electrification process, it is necessary to develop an efficient energy storage system and a stable power transmission system to improve the reliability and extend the endurance of electric aircraft. This paper designs a novel propulsion system topology and power distribution algorithm for light manned electric aircraft. Firstly, a novel aircraft hybrid propulsion system topology is designed, in which the battery energy storage system can work synergistically with the fuel cell to provide power to the aircraft electric engine. Then, an adaptive energy management framework is developed to distribute the aircraft power requirement between energy storage devices. Meanwhile, an aircraft power balance state recognizer is designed to enhance the dynamic performance of the aircraft and adjust the working state of the propulsion system. The proposed hybrid propulsion system configuration and power distribution algorithm are verified under a prototype two-seater electric aircraft: Alpha Electro. Numerical analysis results indicate that the developed methods can dynamically meet the power requirement of aircraft under fast-charging and peak power requirement scenarios. With the developed hybrid propulsion system, most of the fuel cell high-power working points are moved to the medium and low area, which indicates that the fuel cell is effectively protected. Furthermore, the quantified hydrogen consumption can be reduced by 7.63% comparing to fuel cell electric aircraft.