Search

Energy supply capability evaluation is of great significance to the planning and construction of integrated energy systems. In existing energy supply capability evaluation methods for the integrated energy systems, N-1 security at peak load is treated as a rigid constraint. It is not conducive to the effective utilization of equipment, and the impact of integrated demand response is not fully considered. To address these problems, this paper proposes a new evaluation method for the energy supply capability of regional electricity-heating energy systems, which models reliability as a flexible constraint and considers integrated demand response. First, this paper defines the energy supply capability of regional electricity-heating energy systems. Considering the structures of electricity and heat distribution networks, a reliable energy supply interval model is constructed. The maximum energy supply capability can be described by the boundary of the energy supply interval. Secondly, the impact mechanism of integrated demand response and multi-energy load transfer on reliability is extensively studied. A solution method of reliable energy supply interval boundary is proposed. On this basis, a fast reliability evaluation method for operating points is proposed. It then quantifies the load that the system can increase or need to reduce under a certain reliability constraint. In the case study, a real regional electricity-heating energy system is used to validate the proposed method. It is found that both relaxing reliability constraints or increasing integrated demand response participation can enhance energy supply capability of integrated energy systems.

Control of integrated utility grid plays important role, as renewable energy sources are rapidly increasing with installation of different Distributed Generation (DG) systems. Issues arising during the operation like interruption, disorder and uncertainty of network to be resolve. One of the situations is island state, and require to secure human beings and converter devices those are working in integrated energy sources environment. Fuzzy sets are to be identifying this state and these are updating with device control parameters of network like voltage, frequency. This paper presents a k-nearest neighbour Machine Learning ( k n n − M L ) to obtain Upper Limit (UL) Lower Limit (LL) of parameters for continuous fuzzy based state estimation. Also proposed k n n − M L helps to overcome existing challenges and motivate to better recognition of integrated network state. Outcomes of the paper, to update device control at every Abnormal Mode (AM) of operation and as when system parameter changes at different conditional operating modes. Non Detection Zone (NDZ) is also effectively reduced as per with knn parameter in both [LL, UL] mismatch cases. Mismatch cases of 50% to lower value of 10% is also successful tested with proposed method and a proper trip signal has been generated even at lower mismatch conditions. Effective outcome has been obtained with k n n − M L soft computing algorithm.

This paper proposes a comprehensive framework to investigate the potential role of grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) in improving the economy and reliability of the distribution system. For this aim, we first develop an empirical operating model considering operating characteristics of the distribution system with the BSS explicitly. Then, on this basis, a rolling optimization scheduling model of the distribution network is established. In particular, a refined BSS model including a new calculation method of available generation capacity (AGC) is included. Finally, a quantitative method to quantify the effect of BSS on reliability and economy of distribution networks is proposed. Compared with existing works, the main contribution of this paper is threefold. (i) A battery-level BSS operational model including AGC calculation is proposed to fully explore the V2G potential of the BSS. (ii) A rolling optimization scheduling model is established, which fully exploits the potential interaction between economy and reliability of system operation. (iii) An assessment framework is proposed to analyze the special value of BSS resources from aspects of both economy improvement and reliability enhancement, which can provide more practical instruction for BSS planning. Numerical studies are conducted based on IEEE 33-bus distribution network to verify the accuracy and efficiency of our approach. The simulation results show that if V2G of the BSS is orderly exploited and managed, the net profit of the distribution system will increase by 11.97%, and system expected energy not supplied (SEENS) will decrease by 14.34%. The reliability and economy are two contradictory factors, the dispatch model we proposed can achieve better economy with the same reliability level. The location of the BSS also has important effect of the economy and reliability of distribution system. © 2017 Elsevier Inc. All rights reserved.

This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system’s energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively.

The integration of advanced Information and Communication Technologies (ICTs) in the conventional electric power grid is evolving into a Cyber-Physical Power System (CPPS). The seamless integration of control, communication, and computing operations allow CPPS to be fully monitored and controlled. Threats, vulnerabilities, and catastrophic attacks will intrude as CPPS monitoring, protection, and control functions advance. For the CPPS to operate safely, securely, and efficiently, sustainable cybersecurity solutions must be developed, and the power grid's reliability and resilience must be maintained even when exposed to unfavorable network conditions. While more distributed and renewable energy sources are connected to the grid, cybersecurity helps to ensure the supply of electricity is sustainable and of high quality. The validation of such sustainable cybersecurity analysis goes beyond the traditional power grid network analysis, which means that the test should integrate the physical and cyber system behavior and respond to the network attacks. To analyze the cybersecurity and cyberattacks in the practical CPPS, comprehensive and realistic CPPS testbeds are needed. The heterogeneous nature of the CPPS concept urges multidisciplinary testbeds with various functions and capabilities to evaluate the new cyber-attacks, vulnerabilities, and threats in CPPS. Using this CPPS testbed framework, the different types of cyber-attack can be detected, and detection algorithm can be evaluated, and helps to enhance the development of sustainable cybersecurity defenses for realistic CPPS environment with the increasingly dense integration of distributed energy resources (DERs). This Part-I paper review the CPPS testbeds in the view of the physical power system layer, sensing layer, communication layer, control layer, application layer, test platforms, and research goals with the fusion of cyber and physical systems. In addition, this review presented an overview, structure, and application-based evaluation of existing testbeds from the industry and institutions. The various research areas in CPPS are reviewed first to show the research trends on different aspects of CPPS which have gained significant attention in the past decade. The necessity of testbeds for cyberattacks and sustainable cybersecurity analysis in CPPS are then described. Finally, the NIST framework for CPS, CPPS domains, and research areas are presented. Further the Part-II paper will review the classification, overview, and assessment of CPPS testbeds.

This paper first analyzes the impact of the volatility of distributed power generation (DG) output on distribution network planning. This impact mainly includes three aspects: system equivalent load forecasting, distribution network planning decision, and stable operation of the system. To reduce the impact of this instability, this paper takes the wind power system as an example, and establishes the corresponding relationship model of the output state-year output scenario. Secondly, combined with the life cycle theory and multi-scene partitioning, a comprehensive decision-making objective function of the distribution network planning scheme for a single area considering investment operation costs, additional backup costs, energy-saving, and environmental protection benefits, and system reliability costs were established. Because of the many parameter calculations and selection judgments involved in the decision model, an intelligent decision framework based on Dempster-Shafer evidence theory and Multi-agent system (DS-MAS) is proposed after the decision target is established, that is, which can automatically select the fitting selection analysis parameters according to different conditions. Finally, the effectiveness of the model is verified by a practical example.

The paper analyzes grid strength and grid impedance for systems with significant penetration of power electronics. The work shows that the traditionally employed Thévenin equivalent cannot capture the different saturation states of power converters and therefore some classical definitions of grid strength based on short-circuit power at the point of connection fail to describe the system behavior. The paper proposes to map the impedance for all the different possible voltages at the point of connection and analyzes how the impedance depend on the voltage and angle of derivation for different possible simplified systems (grid-following converter in parallel with a Thévenin equivalent and grid-forming converter) considering the different saturation states of the converter. Numerical results and case studies are included as examples of the conducted analysis.

In recent years, the penetration of decentralized wind power generation in distribution networks has increased rapidly, and the uncertainties in wind power generation has posed great challenges on the operation of distribution networks. To this end, this paper establishes a distributed coordinated optimization model of integrated electricity-gas distribution networks considering power-to-gas (P2G) units and gas turbines to deal with the uncertainties of wind power output. First, this paper uses chance constraints to deal with the uncertainties of wind power output and converts it into a linear model that is easy to be solved. Secondly, considering the electricity distribution network and gas distribution network as different stakeholders, optimization models are established with the objectives of minimizing their respective operating costs, and the established models are solved in a distributed manner through the analytical target cascading (ATC). Finally, an integrated electricity-gas distribution system composed of the IEEE 33-node distribution network and 24-node natural gas network is used for simulation, which validate the proposed model and method.

This paper intends to give an effective hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices. The distribution automation platform consists of automatic voltage and VAR control and automatic fault management systems. The objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs. The scheme is constrained by linearized AC optimal power flow equations and planning model of sources and distribution automation. A stochastic programming approach is also implemented in this paper based on a hybrid method of Monte Carlo simulation and simultaneous backward method to model uncertainty parameters of the understudy model including load, energy price and availability of network equipment. Finally, the proposed strategy is implemented on an IEEE 69-bus radial distribution network and different case studies are presented to demonstrate the economic and technical benefits of the investigated model. By allocating the optimal places for sources and distribution automation across the distribution network and extracting the optimal performance, the proposed scheme can simultaneously enhance economic, operation, and reliability indices in the distribution system compared to power flow studies.

Recently, the blockchain technology has attracted widespread attention due to its advantageous features, e.g., decentralization, transparency, traceability, and immutability. To make full use of renewable energy resources, new generation energy systems advocate the deep integration of information technology in real-world energy projects, among which blockchain has become one of the most used technologies. However, with the continuous development of related studies and projects, blockchain has begun to expose more and more limitations. As a result, the application of energy blockchain, i.e., blockchain applied in energy systems, is facing various challenges caused by these limitations. This paper briefly reviews popular application scenarios of energy blockchain, analyzes generic limitations of blockchain and their impacts on energy systems, and looks into possible directions that could deal with these limitations for future blockchain-based energy systems. We argue that blockchain is not a panacea for energy systems because blockchain’s component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems. As far as we know, this paper is one of the few works that deeply analyze the shortcomings of the blockchain technology and potential countermeasures in the energy field.

With the development of the active distribution system with multi-microgrids, increasingly numbers of distributed energy resources are integrated into the distribution network through multi-microgrids. As a result, the economic dispatch problem presents high dimensional complexity. Hence, this paper proposes a decomposition and coordination calculation method to reduce the computational dimension and improve the calculation accuracy. In this paper, the active distribution system with multi-microgrids is divided into two parts: one is the outer power grid and the other is the inner power grid, and the optimal value is obtained by applying the differential evolution algorithm. To improve the efficiency of the traditional network loss calculation, the network loss sensitivity is adopted to calculate the network loss. In order to verify the effectiveness of the proposed method, the test model is constructed based on the IEEE 33 node and 69 node distribution systems. The simulation results show that the proposed method can obtain the global optimal solution, and its economy is better than the overall centralized calculation method. This method is conducive to the subsequent application in the actual power grid system.

New energy centers such as wind power and photovoltaic power generation are generally too far away from the load center. Half wavelength AC transmission technology can solve this problem. However, harmonics may be generated when new energy is connected to half wavelength transmission system. Harmonics will do harm to the normal operation of power grid. Therefore, this harmonic resonance problem is studied in this paper. Firstly, this paper expounds the principles of classical resonance analysis method and modal analysis method, and explains the advantages of modal analysis method through analysis and calculation. Then, the grid connected resonance of large-scale wind farm and photovoltaic power station connected to half wavelength transmission system is studied by modal analysis method. The effects of transmission line length, the number of groups of wind power and photovoltaic inverters on typical modal harmonic resonance are analyzed. The results show that the length of half wavelength transmission line and outgoing line only affect the low-frequency harmonic resonance of some modes; The length of collector cable has an effect on the harmonic resonance of each typical mode; The number of wind power and photovoltaic inverter groups has an impact on the harmonic resonance of some modes, but will not change the location of resonance.

Traditional integrated energy market has problems such as single transaction type, opaque dispatch subsidies, and lack of institutions that can provide professional and fair services in the integrated energy market. Therefore, based on the smart contract of blockchain, this paper designs the cloud service platform for integrated energy market. The participants in the integrated energy market are divided into integrated energy suppliers and integrated energy users. The two can use the Digital Currency Electronic Payment (DCEP) to trade energy (carbon) in the blockchain smart contract provided by cloud service platform, as well as implement decentralized intelligent dispatch by using blockchain Internet of things technology. The smart contract designed by Antchain is used to simulate the energy trading and dispatch process of cloud service platform, and the verifiable simulation results are given. It is proved that the integrated energy market cloud service platform based on the blockchain smart contract designed in this paper can effectively realize not only the security and convenience of energy trading, but also the efficiency and intelligence of energy dispatch.

The active role of distribution system operators (DSOs) coordinated with the transmission system operator (TSO) is highlighted by increasing the competition of new parties in energy markets. Besides, combined heat and power (CHP) units are the primary heat supplier in district heating systems, and their electricity production is strongly coupled with heat productions. This paper evaluates an integrated model for scheduling electricity and heat with considering TSO–DSO cooperation to increase the operating efficiency in the day-ahead horizon. Also, this paper considers the cooperation of the electrical TSO, electrical DSOs, and district heating systems’ operators. The proposed model facilitates energy transactions between systems by considering intermediary variables. The share of each market party is calculated using intermediary variables and locational marginal prices of electrical and heating systems, and the values are compared to the cases with the isolated operating of energy systems. Also, the model considers thermal energy storage systems (TESs) and the heat transaction capability between neighbor systems, and the feasible convex region is used for the operation of CHP units. The DC power flow equations are used at the transmission level, while the AC power flow is used for distribution grids. The AC power flow equations are relaxed into the second-order cone programming (SOCP) formulation, which results in a mixed-integer second-order cone programming (MISOCP) problem. The proposed model is applied to the modified IEEE 24-bus test system, which contains electrical and heating systems at the distribution level. The result shows that the proposed model successfully reduces the operational costs and energy prices compared to the isolated scheduling of energy systems. Also, the model facilitates energy trading between market parties.

Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods.

Dynamic pricing strategy will play an increasingly important role in solving the contradiction between power supply and demand under the current electricity market environment. The performance of different users varies from the same pricing strategy, which further to exacerbate the difficulty of the optimal power dispatching. Therefore, a non-cooperative Stackelberg model based game theory is developed in this paper, which considers both the impact on load fluctuations in power grid and users’ dissatisfaction with electricity consumption. Firstly, users are divided into different types by induction and classification in this model to realize the comprehensive consideration of different types of electricity users. Secondly, two utility functions are set up in this model. The utility function of power supply side is set to represent the benefit obtained in the process of power supply by the power company. The utility function of the power demand side is set to express the dissatisfaction degree of electricity users. The developed model is solved by using the NSGA-Ⅱ algorithm in this paper. Finally, the developed model is applied to the actual case and the sensitivity analysis of relevant parameters is carried out to verify its effectiveness.

With the rapid growth of solar energy generation, lightning hazards to photovoltaic (PV) plants have received attention increasingly. Many PV plants are built in the transmission corridor, leading to an increased occurrence of lightning damages. These damages are caused by lightning strikes to the transmission line nearby, which have not been well addressed in the literature. This paper investigates the transient behaviors of a practical PV plant when a nearby transmission line is struck by lightning. Three types of lightning damages are investigated, namely failure of PV inverters, breakdown of bypass diodes, and arcing between metallic parts. Both bypass diode breakdown and arcing-related incidents in the PV systems have not been analyzed systemically in the literature. In this paper, the numerical results of lightning transients are presented. The simulated induced voltage is consistent with the observed result in the practical plant. Several protection measures against lightning to the PV systems are proposed to achieve better protection performance.

With the increase of DC transmission voltage and capacity, large dc fault current becomes an important issue due to the bottleneck of the capacity of the DC breaker. In order to solve the problem, we have proposed a novel hybrid type DC superconducting fault current limiter (DC SFCL) in our previous papers. Based on a series of previous researches, some improvements are made to design the hybrid type DC SFCL for application in a MMC system in this paper. The target is to limit short circuit current and avoid converter valves blocked when the DC short circuit occurs. In consideration of cost and stability, combined with the transient characteristics and parameter matching of the DC SFCL, an effective optimization design method consisting of electromagnetic theory and intelligent algorithm is proposed, which can obviously promote the utilization efficiency and stability of YBCO coated conductors. According to the optimization design method, a design scheme of the DC SFCL is put forward. Combined with three operation states of the DC SFCL (normal state, current limiting state I and current limiting state II), a corresponding finite element model is established to simulate and evaluate the performance of superconducting coil in the DC SFCL, which further reveal the AC loss characteristics of superconducting coil during current limiting process.

Frequency Disturbance Events (FDEs) occur due to various events such as Generator Trip (GT), Line Outage (LO), and Load Disconnection (LD), which affect the stability of power systems. Depending on disturbance severity, it has small to severe effects on an integrated system's performance. Accurate and fast detection and classification of the event and its location are crucial in monitoring resource adequacy, preventing economic losses and blackouts. This paper, focusing on an online cutting-edge hierarchical methodology, first detects the event, then identifies its classification. Eventually, the exact location of FDE is found based on Phasor Measurement Units (PMUs) data. Compared to other counterparts, which focused only on classifying or locating events separately, in this paper, both aims are achieved using the suggested novel hierarchical framework. This research employs Deep Learning (DL) advances to develop a Recurrent Neural Network (RNN) model and a Long Short-Term Memory (LSTM) model to distinguish and locate FDEs with significant accuracy. In the research, only a few time-series of Rate-Of-Change-Of-Frequency (ROCOF) received from a limited number of PMUs is used as the DL algorithm's inputs. This hierarchical methodology is tested in New England 39-bus, IEEE 14 bus systems, and modified IEEE 118-bus system. The evaluation results demonstrate the potential application of the proposed models to detect and classify FDEs compared to conventional algorithms and the frequency-based DL model. The proposed models have achieved significant classification accuracy. Simultaneously, the models locate the events with competitive performance in terms of precision, recall, accuracy, and F1-score. © 2017 Elsevier Inc. All rights reserved.

In 2015, “Several Opinions on Further Deepening the Reform of the Power System” was issued. The new round of power system reforms proposes to give full play to the vitality of the power generation-side bidding market, which will help to establish a fair, reasonable, and active power market. Due to factors such as a lack of peak shaving capacity and imperfect market mechanisms, China's renewable energy participation in the power market faces many problems. To solve these, this paper proposes a two-level optimization model for a wind power plant and thermal power unit to participate in the medium and long-term electricity market, and day-ahead market transactions. First, the paper proposes the electricity quantity and electricity price determination method of annual bilateral negotiated transactions, monthly centralized bidding transactions and listings, and delisting transactions, and briefly describes how to decompose contract electricity into the day-ahead market in the medium and long-term market. Then, two ways for the wind power plant and thermal power unit to participate in the power market are proposed. With maximization of the market benefits as the objective function, and through comprehensive consideration of the system reserve, wind curtailment penalty, green certificate trading, and other issues, the two models of independent participation and joint participation in market transactions were established to study the problem of maximum profit on the power generation side. Finally, the analysis results of the calculation example show that the joint participation of the wind power plant and thermal power unit in the power market has additional benefits compared to independent participation. The wind power plant can complete the assessment index of the renewable energy quota for the thermal power unit, which does not need to purchase green certificates to complete the assessment indicators. The thermal power unit can provide reserve services for the wind power plant, avoiding output whenever necessary, reducing the cost of the curtailment penalty, and overcoming the threat of wind power plant output fluctuations to the system. The two alliances have greater profit margins.

This paper presents a methodology to optimally share the available grid capacity among customer assets connected within a low voltage distribution grid. Distributed energy resources (DERs) and a new generation of loads such as heat pumps, thermal, hydrogen, electric storages, and vehicles are increasingly being connected to distribution grids. These DERs and loads are intermittent and it is essential to optimally control them for the safe operation of the grid. Additionally, there is increased interest in the local generation, production, trading, and consumption of energy. New regulations to establish local energy communities (LEC) have come to fruition among member nations across Europe. This is to provide a control, market, and legal framework for managing such distributed generators and flexibilities in low and medium-voltage distribution grids and conclusively empower end-users to democratize the energy system. Within a LEC, a local energy market (LEM) is to be implemented. A significant constraint of a LEM or energy accounting system is the grid settlement process. The grid should remain in a steady state when the bids in the market are executed. The methodology discussed in this paper will preemptively stabilize the grid and generate limiting profiles at various locations for individual flexibilities that are part of the local energy market. This is achieved by using an Optimal Capacity Management system which generates limiting profiles at the points of common couplings of various controllable devices in the grid. The controllable devices are required to maintain their active power injection and consumption within the generated limiting profiles to ensure optimum grid level. This will ensure that grid limits are maintained, which are simulated on a test feeder and also applied to a real network model from the Heimschuh pilot site in Styria, Austria.

Adequate power system protection plays crucial role in ensuring reliability of electricity supply, thus it is a key challenge for distribution system operators to increase the effectiveness of protection schemes operation and protection equipment itself. The main goal of the paper is presenting a multi-criteria protection algorithm that may boost effectiveness of power lines’ short-circuit overcurrent relays in medium voltage networks in case of various two phase faults. The idea of enhancing protection performance is based on fault current symmetrical components’ analysis and specifically on using negative and zero sequence currents as criterial values for overcurrent line protection used against such faults. The paper describes in detail the proposed solution and analyses its performance based on medium voltage network simulations carried out using DIgSILENT PowerFactory software. Promising simulation results can be observed for definite time overcurrent protection relays used in reactance-earthed networks, as introduction of the proposed method proved to increase the zone of effective operation twice comparing to the traditional phase current based solutions. Moreover, in the proposed solution, coordination of protection settings for small earth-fault current values, which are identified by other protection relays, is not necessary. The proposed solution may complement the traditional algorithms for short-circuit protection (I≫) used in modern protection relays monitoring the level of negative and zero sequence current, and therefore may significantly improve the effectiveness of detecting various phase-to-phase faults in the considered MV line protection zone.

Nowadays, the application of non-linear loads such as, rectifiers, electronic phase control, PWM drives, gives rise to a vastly increased level of harmonics in the power network. As such, the transport currents of transformers are non-sinusoidal alternating currents. On the other hand, AC losses are one of the most critical characteristics for the design and operation of high-temperature superconducting (HTS) transformers. The amount of hysteresis losses is a key parameter for the design of HTS windings affecting equipment, cooling cost, efficiency, and transformer economic feasibility. The computation of AC losses by traditional techniques, is suitable for the sinusoidal current condition. While, non-linear loads brings about different number of peaks and amplitudes in the current waveform passing through the HTS transformer windings. So that, in addition to the major hysteresis loop, a number of minor hysteresis loops are also formed changing the hysterical nature of the AC losses. Hence, this paper proposes an accurate, yet efficient analytical algorithm, for calculating hysteresis losses in a three-phase HTS transformer's windings in the presence of the harmonic loads accounting for the AC losses estimation. This is accomplished by performing a numerical Finite Element Method to verify the analytical results. The analysis of the hysteresis losses in an exemplary 315 kVA three-phase HTS distribution transformer for different harmonic loads, is another object of this paper.

The electricity market reform in China stimulates the capacity of demand-side resources regulation and provides a new approach to promote large-scale renewable energy access to power grid. However, there is no effective modeling method to describe the participation of demand-side resources in power grid regulation, which seriously affects the research on the response strategy of demand-side resources under the electricity market environment. Aiming at the problem above, this paper presents a multiple hierarchical and multiple time scales modeling method of demand-side resources based on cyber-physical system theory. First, a multiple hierarchical and multiple time scales modeling framework of demand-side resources based on cyber-physical system theory is proposed. Second, on the basis of the modeling framework, the multi-dimensional variable coupling relationships of the energy flow, the information flow and the capital flow of demand-side resources are analyzed. Finally, an example of feasible modeling method under this framework is presented, and the correctness of the presented modeling method is verified. This paper gives full consideration to the coupling relationship of multiple hierarchies, multiple time scales and multi-dimensional variables of demand-side resources under the power market environment, and proposes a demand-side response resources model based on cyber-physical system theory, which lays a solid foundation for future research on demand-side resources' participation in power grid regulation.

The propensity to utilization of inexpensive, clean and distributed energy resources in the today's power systems structure are expanding due to various economic, political and sociological reasons. However, the power generated by these resources is extremely unpredictable, which is called uncertainty and exceptionally fluctuated drastically that is known as variability, during the operation epoch. So, exposure to these two features is great significance and value. This problem is known as the power system flexibility in the literature of electrical power system studies. Promoting flexibility in the system is too expensive and can be evaluated in different time frames. This paper studies the power system flexibility with the presence of uncertainty and variability of demand and supply side resources in a planning timeframe. This paper proposed an improved method for demand response uncertainty modeling with Z-number. This method is a hybrid possibilistic and probabilistic process. Furthermore, this paper applied an approved method for renewable generation uncertainty modeling with Monte Carlo. The scrutinies show, the most cost-effective way to consider the flexibility is planning timeframe, so this study proposed a novel paradigm for Flexibility-Constrained Integrated resource planning in presence of high penetration uncertain and variable demand-side and supply-side resources namely as FCIRPU/V-DSRs. The simulations are implemented with the proposed FCIRPU/V-DSRs methodology on a test system. The results show that this innovative framework provides a significant improvement in the power system flexibility at an inferior cost than other investigated methods.

Hybrid modular multilevel converter (HMMC) topology consisting of full-bridge submodules (FBSMs) and half-bridge submodules (HBSMs) is one of the promising technical solutions for long-distance high voltage direct current (HVDC) power transmission systems. However, the HMMC has very different charging circuit characteristics from the conventional half-bridge MMC (HB-MMC) topology and suffers from low and unbalanced precharge capacitor voltages and surge current when starting from the AC-side, due to its hybrid submodule configuration. In this paper, an AC-side startup scheme for the HMMC is proposed, including an additional semi-controlled charging process and an improved controlled charging method, which allows the HMMC to smoothly charge its submodule capacitors and establish the DC-link voltage without requirements of auxiliary sources. The improved controlled charging method is composed of a group modulation and a group voltage balancing control strategies, which enables the HMMC to operate stably with enhanced AC voltage output capability under the condition of low and unbalanced capacitor voltage in the HBSM group and FBSM group, thereby eliminating converter overmodulation and surge current during the startup procedures. In addition, DC-side startup strategy is also discussed in this paper. PSCAD/EMTDC simulation results based on a practical ±200 kV/750 MW HMMC-HVDC system demonstrate the effectiveness of the proposed method.

This paper proposes a novel impedance source modular DC/DC converter for the energy storage system (ESS), which overcomes the drawbacks of traditional modular multilevel DC/DC converter (MMDDC), such as discontinuous current on energy storage side, easy over-current in charge mode, large number of sub-modules etc. A new quasi-Z source (qZS) sub-module is constructed by combining bi-directional quasi Z source and half bridge converter. The bi-directional qZS can make the input voltage of half bridge sub-module controllable, and then solve the over-current issue of the traditional MMDDC in charge mode. Meanwhile, the boosting function of the impedance source can help to reduce the number of sub-modules when the system is applied to the medium and high voltage occasions, simplifying the system structure and improving system efficiency. More importantly, the bi-directional qZS can make the current on energy storage side continuous, improving the lifetime of energy storage device. Detailed analysis about the operation principle and characteristics of the proposed converter are presented in the paper, and the study of control strategy design for the proposed converter is also carried out. At last, the effectiveness of the proposed converter and control strategy is verified by experimental results.

This paper presents a new model for the self-scheduling problem using a home energy management system (HEMS), considering the presence of solar photovoltaic (PV) panels and an air conditioner (AC). The AC used in this study utilizes an inverter for controlling the temperature. The proposed model adopts the time-of-use (TOU) tariff for electricity, and minimization of the daily bill is the main objective of the proposed model. In the proposed model, there are some fixed and flexible loads, and optimal scheduling of the home appliances is modeled as a mixed-integer linear programming (MILP) problem to achieve the minimum daily bill. The HEMS in this paper incorporates a PV system, combined with the electrical energy storage (EES) system to deal with the uncertain solar power generation as well as optimally serving the loads during the peak hours, taking into account the charging and discharging control strategy of the installed EES. The indoor temperature can be held within a predefined margin and the settings of the AC system may be obtained according to the indoor-outdoor temperature model, which has been presented in this paper. During peak hours, the contribution of the AC in energy consumption to the daily bill is considerable. Therefore, optimal operation of the AC and other home appliances can effectively reduce the energy consumption and consequently, will result in reduced bills.

One of the most significant current topics in the energy market is the decentralized operation of the power system wherein the entire discipline is constructed based on the consensus concept. In such a framework, an agreement among all effective participants in the market should be brought without the presence of an independent arbitrator. This paper proposes a secured energy market architecture based on peer-to-peer (P2P) idea to provide an appropriate platform based on the decentralized network and key aspects of energy market, including network architecture, interface communication, and network security. It is apparent that communication interfaces connecting the market participants is a fundamental property for achieving this purpose and that must be secured against the malicious attacks. In this regard, reaching the secured consensus is guaranteed by providing a new blockchain platform coincided by P2P energy market. The energy market is conducted by an effective Relaxed Consensus-Innovation (RCI) based algorithm with the aim of bringing the power/price exchanged among connecting participants in the form of P2P structure. In the proposed model, a microgrid and a smart grid are considered as the market participants, who tend to negotiate with each other in a way that follow their own benefits in the secured environment. The microgrid includes the wind turbine (WT), photovoltaic (PV), tidal turbine and storage unit to satisfy its demand and the smart grid is composed of distributed generations (DGs) and lines in the form of the IEEE 24-bus test system. In order to handle the uncertainty effects in the problem, a stochastic framework based on unscented transform (UT) is proposed in the P2P energy market. In order to assess and verify the fault-tolerant system ability against the cyber-attack, the fault data injection attack (FDIA) is modeled and applied to the P2P energy market in a blockchain platform. The simulation results approve the appropriate performance and applicable nature of the proposed concepts in this paper.

The penetration of renewable generation, such as photovoltaic and wind power in power networks will significantly impact the system’s stability in future, due to randomness and intermittency in the natural environment. Moreover, the correlations between renewable generation may enhance the impact on power system's stability. In this paper, cumulant-based maximum entropy method (CMEM) combined with Nataf Transform (NT) is proposed to analyze the steady-state voltage stability problem considering correlations and uncertainties of power injections and consumptions. The proposed methodology is tested on the IEEE 30-node and IEEE 57-node test systems. Taking the results of Monte Carlo method (MCM) as the experimental control group, this paper compares the proposed method with series expansion method (SEM), discusses and analyzes their calculation results and speed under different scenarios, and the results of the comparison prove the validity, accuracy and rapidity of the CMEM.

To deal with the deep penetration of renewable energy in the power grid and energy coupling in the distribution and consumption part, improve energy efficiency, and construct the regional integrated energy system (IES) with multiple self-sufficient cells, energy cell has attracted widespread interest. However, the different management systems, diversified energy features, and information barriers increase the difficulties of unified energy flow calculation of IES. Based on the theory, this paper proposes a parallel distributed computing method for multi-region IES with electricity network being considered as a dividing surface. First of all, the basic principle of energy cell is proposed and the large-scale IES is divided into several energy cells according to the energy balance ability and geographic location. Then virtual nodes are added at the boundaries of the energy cells and the adjustment factors are introduced to ensure system convergence. Moreover, the extended Jacobian matrix was constructed to calculate muti-energy flow (MEF) in a single energy cell and decoupled energy cells can be computed in parallel through the limited information exchange of virtual nodes. Finally, two cases are utilized to verify the performances of the proposed energy method. Comparing with the integrated method and the decomposed method, the proposed method in this paper has better abilities in calculation efficiency and accuracy.

This paper proposes a power regulation scheme for dispatchable grid-connected distributed energy resource (DER) units based on the iterative learning control (ILC) strategy. The proposed control provides fast and stable control of the real and reactive powers that the DER unit delivers to the grid. In addition, it compensates the harmonic distortion of the grid current caused by the local nonlinear loads and delivers a high quality power to the grid. To this end, the paper presents the mathematical model on which the proposed control is based, as well as the control design methodology. A new MIMO iterative learning control with perfect power tracking and harmonic rejection performance as well as minor measurement requirements is proposed. The performance of the proposed ILC strategy is demonstrated through time-domain simulation of a grid-connected DER unit under various operating scenarios, and it is compared with the performance of the conventional control based on the instantaneous power theory (IPT).

Robust optimization (RO) is one of the most effective methods to deal with unit commitment problems of power systems. The most commonly used robust optimizations are classical robust optimization (CRO) and Seng-Cheol Kang robust optimization (SCK-RO). In order to better coordinate the robustness and economy of power system operation, this paper introduces another robust optimization model called multi-band uncertainty robust optimization (MBU-RO). The proposed multi-band uncertainty robust optimization divides the wind power fluctuation interval into several small intervals and weakens the effect of intervals with less probability on robustness and economy of power system operation. In this paper, the basic idea of multi-band uncertainty robust optimization is introduced. The differences between multi-band uncertainty robust optimization and Seng-Cheol Kang robust optimization are analyzed. Besides, this paper improves the parameter setting method of multi-band uncertainty robust optimization based on the historical sample of wind power so that the optimization result of multi-band uncertainty robust optimization is more objective. The results are tested on the IEEE-39 power system with one wind farm and the IEEE-118 power system with three wind farms. Compared with the classical robust optimization and Seng-Cheol Kang robust optimization, simulation results show that the proposed multi-band uncertainty robust optimization can better balance the robustness and economy of the power system with wind energy.

During the heating season in the “Three North” area of China, the wind curtailment has become a serious problem due to the lack of space for grid-connected wind power. Firstly, from the perspective of improving the acceptance capacity of wind power curtailment, this paper establishes a model of hybrid energy storage. Then considering the influence of the frequently changing electrodes of the regenerative electric boiler on its working life, this paper introduces the optimization coefficient of electrodes, and the optimal operation strategy of hybrid energy storage system is put forward. Finally, based on the actual data of 200 MW wind farm, this paper compares and analyzes the advantages and disadvantages of different control methods. The results show that the proposed method can further improve the power system's ability to accept wind power, under the requirements of meeting the heating demand and reducing the number of electrode adjustment of electric boilers.

Electricity theft is a problem that affects the efficiency and profitability of power companies. There are several studies and applications in order to detect electricity theft, including the use of artificial intelligence techniques and the most recent deep learning methods. For problems like it, the datasets utilized are completely unbalanced – consequently, the use of metrics as accuracy is not enough to properly evaluate the performance of the method for the application. In the present paper a Convolutional Neural Network (CNN) is applied to electricity theft detection problem using several techniques for balancing the classes of the dataset: Cost-Sensitive Learning, Random Oversampling, Random Undersampling, K-medoids based Undersampling, Synthetic Minority Oversampling Technique, and Cluster-based Oversampling. The objective is to compare and select the best unbalanced data-handling technique for CNN, utilizing a specific metric for problems with extremely unbalanced classes – the AUC (Area Under Receiver Operating Characteristic Curve). The results present that some techniques combined to CNN reach values of high quality, comparable to the obtained by other classifiers. Finally, the paper points studies related to electricity theft detection must deal with the unbalanced characteristic of the dataset in order to achieve better (or, in other words, correct) results.

Increasing penetration of renewable energy resources has led to the need for operational flexibility of the power system to cover the uncertainty and variability of these resources. Therefore, employing flexible resources during the operation time will play an important role in nodal power balancing, providing the required ramping capacity, and reducing the operating costs of the power system. Some of the most important flexibility resources in the power system are fast-response resources such as gas and combined cycle units with the ability to provide non-spinning ramping capacity, bulk energy storage systems with the ability to switch from charge to discharge mode and vice versa in short-time period, and demand response programs with fast-response to price and incentive signals, which prevent the occurrence of severe ramps by correction of the system load factor. Therefore, contrary to the existing literature, this paper examines the impact of the above-mentioned fast-response resources on the IEEE 6-bus and IEEE RTS24-bus test systems both on the generation side and the demand side in form of the proposed model for day-ahead scheduling problem under high penetration of wind-based units. Another important point is the need to co-optimize energy and required ramping capacity reserve of the system with efficient accuracy and tractability. Thus, in this paper, a mixed-integer, two-stage and tri-level robust model with adaptive approach has been utilized, which is solved by nested column-and-constraint generation decomposition-based algorithm in order to jointly clear the energy and ramping capacity reserve with the presence of fast-response resources. The results show that utilizing the potential of fast-response resources with the aim of providing operational flexibility reduces operating costs by up to 10.61%.

This paper proposes a novel evaluation method for the risk of simultaneous commutation-failures (CFs) in multi-infeed high-voltage direct-current (HVDC) systems, which takes into account the DC current rise caused by the inverter AC system fault. By calculating a multi-infeed interaction factor (MIIF), and using an equivalent model for multi-infeed HVDC systems. The effect of DC-current on commutation failure is analyzed and the factors that affect the simultaneous commutation-failure are determined. Based on the equivalent equation for both the DC line and DC control, a time-domain expression for the DC-current caused by the inverter AC system fault is calculated. Based on this, this paper describes a novel CF evaluation method that takes into account DC current rise by solving for the maximum of the DC current. Several useful parameters like the MIIF, the critical multi-infeed commutation failure factor (CMICFF), and the multi-infeed commutation failure factor (MICFF) are defined, and a method for the risk of simultaneous commutation-failure in multi-infeed HVDC systems by comparing CMICFF and MICFF is proposed. The accuracy of the method proposed in this paper is validated by a comparison with a simulation using the software PSCAD/EMTDC.

In order to solve the problem of insufficient smoothing ability and excessive smoothing, this paper proposes a method combining exponential smoothing short-term wind power forecasting with wavelet packet decomposition. According to the optimal decomposition level, the output power in line with the grid connection standard can be obtained. To improve the service life and reliability of HESS, this paper further introduce the concept of standby storage. Then, this work propose a method combining CEEMDAN with NEE and NSE to achieve the optimal power allocation instructions of the HESS. According to the residual uniqueness and adaptive frequency division, the IMF and residual components of different orders can be achieved. Then, the cut-off point with the largest proportion of energy will be obtained by NEE and the cut-off point with the lowest self-similarity by NSE, which leads to the reasonable power allocation. In the simulation and experiment analysis, it further verifies that the introduction of standby storage element can effectively reduce the charge and discharge times of the HESS, decrease the fluctuation index of SOC, which improve the service life and identification accuracy of HESS. The result shows a certain feasibility and actual significance in the practical applications.

Autonomous microgrids are a suitable solution for off-grid electrification in terms of costs and reliability. The correct sizing of its generation and storage systems ensures efficient utilization of the available energy resources. Generally, many sizing approaches assume optimized energy management strategies that rely on central control architectures. However, these architectures are not always available, especially in limited investment microgrid projects. For this reason, the study of operation scenarios based on decentralized control strategy such as droop control is relevant. Based on this, this paper aims to evaluate the impact of the droop control over the sizing results of a solar/wind/battery/diesel microgrid. For this purpose, a case study of a sizing problem is presented, including the formulation and modelling. Results are presented comparing an hourly optimized energy management scenario with multiple values of the droop control parameters. Simulations results indicate that a competitive total cost can be obtained if the droop parameters are calculated considering the microgrid sizing results. Based on this, a generalizable design methodology for this purpose is presented.

Operational flexibility aiming to mitigate variability impacts of Renewable Energy Resources (RES) inherently depends on inter-temporal operational status. Accordingly, in the current paper, a novel operational flexibility maximization procedure is developed within a robust Security Constrained Unit Commitment (SCUC) problem to find the most tolerable flexibility band in response to RES variability impacts. In addition, combined-cycle units (CCU) and incentive based demand response, from both generation and demand sides, are considered as flexibility providers because of their specific characteristics such as high ramping capacities and fast start-up/shut-down rates. However, unlike conventional units with single mode operation, a CCU unit is composedof multiple combustion turbines (CTs) and steamturbines (STs) that can operate in different modes. Since it is needed to decide the on/off statusand power output of each CT and ST unit at every timeinterval, the recourse decision layer will be non-convex. The challenging non-convex recourse decision level is cast as a mixed-integer program that is solved by an exact nested column-and-constraint generation algorithm. The capability of the proposed model is then investigated in standard IEEE 118-bus test system.

There is a trend that traffic unbalance in electric power communication networks is becoming more and more prominent because of the increasing communication demand from the development of the energy Internet. To reduce transmission risk and improve network’s reliability and throughput, it is necessary to optimize the routes of communication services with traffic and risk balance strategies. In this paper, a risk and traffic-based routing algorithm for electric power communication services is proposed. We first establish a risk balance strategy with service importance and link availability, then proposes a traffic balance scheme considering bandwidth on links and traffic-processing capability of nodes. Based on traffic characteristics and service risk, the proposed algorithm considers the traffic flow and risk value of the link synthetically and designs a corresponding routing strategy by introducing the balance factor. Numerical results obtained in a real electric power communication environment demonstrate that the algorithm can reduce both the blocking probability and the service risk simultaneously, which achieves the balance between traffic and risk and obtain the optimal performance of electric power communication network.

As a promising technology to integrate renewable energy and enable decentralized energy management, microgrid (MG) offers an appealing network architecture due to its potential economic, environmental, and technical benefits. However, with the increasing deployment of intelligent devices and the growing network interconnectivity, communication channels and controllers for MGs become more vulnerable to emerging cyber threats. Two types of attacks are considered in this paper, including the data integrity attacks on the system status feedback and time-delay attacks which may disrupt the control of the MGs and lead to adverse consequences. A modified model predictive control (MPC) scheme is proposed for the secondary frequency control of MGs based on the online status switching method and generalized cross correlation (GCC) estimation to detect the real system status and time delay injected to the control system. The Euclidean metric is used in the online status switching method to obtain the real system states. Meanwhile, the GCC based delay estimation is developed to detect and track the time delay posed by attacks in the real-time operation. Case studies under different scenarios of the attacks are conducted, and the simulation results verify the effectiveness of the proposed MPC scheme under the cyberattacks.

The rapid growth of distributed energy resources (DERs) has significantly increased the demand for land resources and power delivery equipment in urban areas. The construction of a multi-station integrated system in the substation area could settle this challenge well. However, its integration will reshape the power flows of urban distribution systems. The approach to reasonably coordinate distributed resources of integrated stations and power supply districts challenges the traditional operation management. In this paper, we explore the capability of the integrated station to join distribution system operation, and collaborate with DERs in its power supply zone to mitigate operational risks. A three-stage operation strategy is proposed for this purpose, considering the adjustable potential of DERs, which would affect reliability performance. The upper stage schedules the integrated station and other DERs. The middle stage coordinates the internal resources of the integrated station and evaluates its equivalent adjustable potential, while the bottom stage would measure the resource regulatory risk to adjust the schedules. OLTC transformer, PVs, BES and flexible loads are coordinated. Finally, the proposed method is validated in a modified PG&E 69-node test system. The results show the effectiveness of the management strategy of the integrated station to grid (S2G), and the voltage/loading violation of the distribution network are mitigated.

The large-scale integration of intermittent and uncertain renewable energy poses challenges for power system scheduling, especially for peak-shaving. In this paper, a multi-objective unit commitment model of jointly concentrating solar power plant and wind farm for providing peak-shaving considering operational risk (RMUC) is proposed. A concentrating solar power (CSP) plant is employed to improve renewable energy consumption and compensate wind power fluctuations. Firstly, an operational risk model under renewable energy integration with intermittency is constructed to quantify risks of wind curtailment and load shedding. Then, a novel RMUC model is established by incorporating operational risk into the unit commitment (UC) model, which can optimally allocate operational flexibility of power systems over spatial and temporal domains to reduce operational risk. The proposed model can co-optimize the uncertainty level and the peak-shaving operation, which is able to obtain an optimal trade-off between peak-shaving effect and reliability. Finally, the proposed model is applied on an IEEE six-bus test system and on a simplified real power system for verification, and the cost-effectiveness of the CSP plant in reducing the operational risk and providing peak-shaving is quantified.

This paper presents a transactive energy market pool framework for facilitating energy trading, with demand response consideration, among a group of participants in a community microgrid using an auction-theoretic approach. The auction theory is used to design the pricing mechanism in the market pool. In the market, the self-interested participants (consumers and prosumers) directly trade their net demand to the neighborhood with a transactive energy market pool operator using the existing infrastructure of the distribution utility. The market operator determines the pool market-clearing price & quantity by optimizing the received power bids and offers from the participants using a periodic iterative double auction pricing mechanism. The optimally traded price & quantity are broadcast to all the participants to perform demand response management and optimize their electricity bill. The optimal positions of schedulable loads in the market time-line are achieved using a binary genetic algorithm while minimizing the electricity bill. A comparative analysis between different pricing mechanisms is provided in terms of performance indices namely self-sufficiency and self-consumption through various case studies. It is found that the proposed auction-theoretic approach improves the rate of community self-sufficiency, rate of community self-consumption, and savings in electricity bills for the market participants compared to previously proposed pricing methods in the literature.

Technology is advancing progressively at a very sharp rate in every phase of the commercial and economic issues, where energy plays a vital role in hi-tech enrichment. After continuous and multi directive research, human is efficient enough to put their steps on the peak of all imaginable pitches. But a human cannot fight against the environment in all instances. COVID-19, or Corona Virus, is such a type of environmental pandemic. Due to a large spread rate of COVID-19, the human society of the entire Globe is getting affected within a very small period. During its first and second wave, it collapses maximum countries health, economic structures along with the electrical sector. Many countries have enforced the ‘lockdown’ to their citizen for minimizing the community spread rate, which directly affects the energy sector. Standing in this scenario, this study focuses on the significant effect of COVID-19 on the energy sector of the Globe, especially in India. This paper also demonstrates that how several countries are trying to secure their power sector in this pandemic situation.

In the energy transition towards a carbon-free society, the continuous changes that energy systems are experiencing, the increasing penetration of renewable generation, and the incorporation of short-term storage technologies such as batteries have increased the effort to model and predict its development and operation. In this context, power system models play a relevant role. However, to be suitable for the decision-making process, these tools should not require a massive computational effort. To overcome this challenge, this paper proposes a new methodology that reduces the temporal dimension of the problem while maintaining accurate results. This methodology is specially designed for medium-term operation of real-size power systems with a significant presence of renewable generation and storage systems. By means of a two-stage clustering algorithm, the proposed approach transforms the temporal structure of the model’s input parameters into different levels of time aggregation. This arrangement makes the problem manageable and computationally tractable. In addition, together with the newly incorporated model formulation, this methodology allows capturing at the same time the short- and medium-term variability present in power systems.

This paper presents a bi-level optimization problem to obtain the optimal location and size of the flexi-renewable virtual power plants (FRVPPs) in active distribution networks (ADNs). The upper-level formulation of the problem minimizes a normalized objective function (a dimensionless function), which is a normalized summation of the expected annual energy cost and expected annual energy loss of the ADN, subjects to linearized AC optimal power flow (LAC-OPF) equations. Also, the lower-level problem minimizes the annual investment and expected operating costs of flexible and renewable sources constrained to the planning and operation model of these sources in the FRVPP framework. The suggested scheme incorporates the unscented transformation (UT) method to model the uncertainties of load, energy price, and renewable power generation. Then, the Karush-Kuhn-Tucker (KKT) approach converts the proposed bi-level problem into a single-level optimization formulation to achieve the optimal solution using conventional mathematical solvers. Finally, the proposed scheme is applied to a standard test system. Simulation results confirm the capabilities and superiority of the proposed strategy in improving the technical and economic status of the ADN while obtaining the optimal location and size of renewable and flexible sources in the FRVPP framework from an economic aspect.

Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a decentralized robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed decentralized implementation framework, a unified robust linear state estimation model is derived for each AC and DC regions, where the regions are connected via AC/DC converters and only limited information exchange is needed. In this context, estimation accuracy may be suffering due to linearization. To enhance the estimation accuracy, a deep neural network (DNN) based on the smart meter data is used to extract hidden system statistical information and allow deriving nodal power injections that keep up with the real-time measurement update rate. This provides the way of integrating smart meter data, SCADA measurements and zero injections together for state estimation. Simulations on two hybrid AC/DC distribution systems show that the proposed DRSE has only slight accuracy loss by the linearization formulation but offers robustness of suppressing bad data automatically, as well as benefits of improving computational efficiency.

The urgency to address the uncertainty in the day-ahead scheduling (DAS) of integrated electricity and natural gas systems (IEGSs) has been highlighted, and the robust optimization has been proven to be an effective method. However, the gas system and electricity system are generally operated by different utilities in practice, i.e., gas system operator (GSO) and electricity system operator (ESO), which poses a considerable challenge to solve this two-stage mixed-integer nonlinear DAS model due to the limit on information sharing. In this paper, a novel distributed calculation method is proposed to solve the robust DAS model of IEGSs in a decentralized way. First, a mixed-integer master problem and a non-convex max–min subproblem can be obtained from the original DAS model by employing the column and constraint generation (CCG) method. Afterwards, based on the limited information exchange between ESO and GSO, an innovative decomposition method is proposed for the master problem to obtain the optimal DAS solution, and then a novel inner CCG algorithm is presented for the subproblem to guarantee the security of IEGSs. By doing so, the robust DAS of IEGS is decomposed into several mixed-integer linear programming (MILP) problems, and GSO and ESO can coordinately solve these MILPs with limited information sharing. Last, numerical tests on two IEGSs verify the superiority of the proposed method in hedging against the wind uncertainty and achieving the solution optimality.