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To evaluate the investment effectiveness of and provide decision-making support for a production and technical transformation project (PTTP) in a power system, the concept and methodology of perfect technical transformation (PTT) are presented in this work. The well-established analytic hierarchy process (AHP) is employed to disassemble the evaluation objectives of a PTTP while the weights and scores of indicators at each level can be determined. Then, the comprehensive evaluation result of a PTTP can be obtained by weighted calculation. A perfection percentage index is defined to judge the satisfaction degree of an implemented PTTP. Next, a deviation identification and improvement method is used to accurately locate the poorly implemented parts of the PTTP, and optimize the technical scheme of the PTTP. Finally, an actual PTTP is employed to demonstrate the presented PTT based approach for investment effectiveness evaluation of a PTTP.

Under the obligations imposed by the Renewable Portfolio Standard (RPS) on the generation side, power companies are facing both challenges and opportunities concerning investment decision-making problems. To begin with, three strategies that thermal generation companies can adopt to meet the quota obligations are considered in this paper: investing in newly built wind farms, purchasing Renewable Energy Certificate (REC) and paying fines for unfulfilled quota obligations, with the equivalent expected return and risk of each strategy analyzed immediately afterwards. Next, based on the portfolio theory, the Portfolio Decision Model of thermal generation companies is put forward and the optimal mixed strategy of the companies is determined. Finally, the operation data of actual electricity market and REC market are served for demonstrating the proposed model. Moreover, sensitivity of distinct variables including risk preference of power companies and quota policy of the government on the results of the optimal mixed strategy are analyzed. The results of case studies show that thermal generation companies can obtain the maximum investment decision-making utility by adopting specific mixed strategies according to different risk preferences and different stages of RPS implementation. Also, the government should increase the unit penalty with the increase of RPS quota ratio.

Due to the problems of climate change and global warming, severe air pollution and energy security, there has been a stable growth of renewable energy integration into power systems during the past decades. This brings significant challenges to both short-term (electricity spot markets) and long-term (long-term bilateral and capacity markets) electricity markets. Against this background, the joint energy and ancillary service electricity market is studied in this paper for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. Meanwhile, the proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Finally, numerical experiments are conducted to validate the proposed market mechanism and algorithms.

For the life-cycle cost management of a power grid project, the maintenance is not only expensive, but also has many impacting factors. Therefore, studying the cost of maintenance stage is important to improve the cost management of power grid enterprises. Based on the life-cycle cost management, this paper analyzes the content and cost composition of various maintenance tasks of power grid engineering. Finally, through the introduction of single factor sensitivity analysis method, combined with specific examples, this paper analyzes in detail the impacts of various kinds of power grid maintenance tasks in life-cycle maintenance.

The popularization and applications of electric vehicles (EVs) are closely related to the convenience and economy of charging services, while the construction of charging facilities must coordinate the interests of users, charging facility owners and the power grid company concerned. In addition, the uncertainty associated with the number of available piles in charging stations caused by the failure rate of charging facilities must also be considered. In this context, a chance constrained goal programming (CCGP) model with good flexibility is first introduced to quantify the uncertainty caused by the failure rate of charging facilities as random risk cost of redundancy of charging facilities and charging users' loss. Secondly, the travel behavior of EV users is simulated dynamically to analyze the cost of charging detour and waiting time. Then a bi-level programming model is established with the minimum total social cost as the upper target and the minimum new capacity expansion cost of distribution system as the lower target. The location and capacity of charging station is calculated by the upper model, and the transformation scheme of distribution system is obtained by the lower model. Finally, the coupling system of 77 node traffic network and 118 node distribution system is used to verify the applicability and effectiveness of the Coordination Planning Model of EV charging station and distribution network.

Modeling the consumption behavior of residential customers is very important for investigating their potentials to participate in demand side management and retail market competition. The Engel-Kollat-Blackwell (EKB) model is well-known in consumption behavior modeling, however it is not directly applicable for modeling residential energy consumption, as explored in this paper. An extension of the EKB model is next developed to explain the behaviors of residential energy consumption, including derived demand characteristics, habit change process, behavior change process, internal feedback, external stimulus factors, and environment factors, and employed to implement a green information system so as to guide the residents in energy transition. A questionnaire survey information APP system on WeChat is implemented and the survey results briefly analyzed.

Active power outputs from distributed generators (DGs) are usually coordinated by using distributed control. However, such networked control systems are vulnerable to jamming attacks, which can cause packet losses in the communication channel and destabilize the system. Given this background, this paper proposes an event-triggered based cooperative control of DGs in a distribution network subject to jamming attacks. The presence of packet losses caused by jamming attacks is characterized by a probability model, and the attacking process is modeled by a stochastic process. The proposed controller design method is presented based on the stochastic stability analysis. Since the aperiodic sampling mechanism is utilized, the proposed method is suitable for the situation with resource-limited microprocessors. Besides, the proposed controller is resilient to jamming attacks and communication failures. Finally, the effectiveness of the proposed method is demonstrated by the IEEE 34-bus test feeder.

With the ever-increasing penetration of distributed generations (DGs), traditional distribution networks (DNs) are facing issues such as two-way power flow, increased voltage fluctuations and complex operating control, which need to be properly addressed. While on the other hand, the active distribution network (ADN) could effectively dispatch DGs, energy storage devices, and demand-side loads, and address these issues. To develop an optimal operation strategy considering interactions among source-network-load-storage in an ADN has become a problem with extensive concerns, and is the subject of this work. The objective is to minimize the total operating cost and the variance of the load curve for a given time period. Then, the well-established NSGA-II algorithm is employed to solve the developed multiple-objective optimization model. Furthermore, the entropy weight method is utilized to weight each term in the objective function, so as to avoid the involvement of the subjective preferences of decision-makers. Case studies are employed to demonstrate the proposed method, and it is shown by simulation results that the attained optimal scheduling strategies can enhance the scheduling flexibility of the ADN by coordinated interactions among source-network-load-storage, and hence the total operating cost and the fluctuation of the load curve are reduced.

The electricity purchase cost of an electric vehicle (EV) charging station (EVCS) with photovoltaic (PV) facilities can be reduced by matching the EV charging load and PV generation output. However, the uncertainties of the EV charging demand and photovoltaic generation output impose difficulties to real-time charging scheduling. In this paper, an EV charging scheduling model based on the spot pricing of electricity with an objective of minimizing the electricity purchase cost of an EVCS is proposed. The historical data of PV generation output and EV charging demands are employed in forming the daily charging optimization model, and the daily optimal charging scheduling is carried out and a training sample set is then attained. The Bayesian network (BN) based real-time EV charging scheduling, that is carried out in a recursive way with one time period ahead considered, is next addressed, and the BN structure is determined by the hill climbing algorithm with Bayes scoring employed. The EV charging schedule is subsequently determined by the Bayesian inference. A case study based on an EVCS in an industrial park is carried out to demonstrate the proposed method, and comparisons between the proposed method and the deterministic real-time scheduling method are also detailed.

Since China is still in the early stage of electricity market reform, some mature demand response (DR) mechanisms cannot be applied to China's electricity market. Based on the consumer loyalty program of the study of marketing, an incentive-based demand response (IBDR) mechanism for residential consumers is presented, which is named as score-based incentive demand response (SIDR). A bilevel SIDR model is built to optimize the profit of an electricity retailer considering active demand response behaviors. An optimal demand response model for residential consumers is presented to describe different behaviors of the residents with power-score and monetary incentives, considering resident differences and psychological accounts. Case studies demonstrate the effectiveness of SIDR for enhancing the profit of electricity retailers.

In recent years, extreme weather disasters have occurred frequently, so reducing greenhouse gas emissions is an urgent task. How to accurately estimate corporate carbon emissions in real-time directly affects this task. Therefore, a real-time estimation framework of corporate carbon emissions based on load identification is proposed in this paper. In the proposed framework, the total carbon emission consists of two parts: direct carbon emission and indirect carbon emission. First, a Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BLSTM) model is presented and employed to monitor the states of devices in a factory in realtime. Then, the direct carbon emission is estimated according to the state and related carbon emission intensity. Meanwhile, the indirect carbon emission can be obtained through multiplying the marginal carbon emission factor by the electricity consumption in the factory. The proposed framework was used in a case study to estimate the carbon emission of a steel plant in real-time, which proved its effectiveness and accuracy.

The big obstacle for residential energy transition is the lack of a consumption behavior model. At first, special characteristics of residential customer are discussed, including their fundamental purpose of energy use and attitudes against constraints and energy prices, as well as derived demand characteristics on appliance in energy consumption, etc. Then, a four-tier energy consumption behavior model of residential customers is developed with “virtual appliance” being generalized into three time scales as current, near future, future, and the behavior evolution process is sectionalized into cognition, consciousness, demand and behavior. The methodology and technology to implement the four-tier model in real applications will be dominated by the emerging cyber physical social system in energy, and a customized questionnaire survey system is demanded to investigate the consumption behaviors of residential customers.

Demand response (DR) can effectively promote the users to adjust their power demands independently, so as to optimize the overall load curve, relieve the pressure of peak load growth, and ensure the reliable operation of the power system concerned. Aiming at the fact that residential users do not have the access to the market quotation and the non-real-time dynamic variability of electricity price under the current electricity market mechanism in China, a new strategy to promote active participations of users in demand-side response, i.e., a score-based incentive mechanism for attaining demand response (SIDR) from residential users, is proposed. First, the load models of electrical appliances in residential users are established, with the comfort degrees of the users taken into account. Then, the cost-benefit analysis is carried out from two market entities: the power grid company and the residential users. On this basis, a bi-level optimization model of score-based incentive mechanism for promoting demand response participations of residential users is established. It is shown by case studies that the proposed SIDR can effectively encourage the residential users to participate in DR and hence enhance the economics of power system operation.

With the development of technology and ever-increasing integration of renewable energy power generation, the growing problem of the uncertainty of power outputs from renewable energy sources (RESs) has further promoted the progress of the demand-side response (DR) technology. The large industrial customers have high power consumption and high degree of automation, bringing in a higher DR potential. This paper presents a DR strategy for large industrial customers considering the uncertainty in the power outputs from RESs. First, the DR model of large industrial customers has been established considering their characteristics. Then, to deal with the uncertainty of the RES outputs, the methods of scenario sampling and reduction are formed, through which the typical and representative scenarios of RES outputs will be obtained. Next, based on the day-ahead prediction of the power outputs from RESs and real-time electricity price, a day-ahead DR strategy has been established for such customers with an objective of minimizing the overall daily electricity cost. Finally, taking an industrial park with distributed wind power and photovoltaic units as a sample example, the feasibility and effectiveness of the proposed method are demonstrated. In addition, the impacts of different types of loads are evaluated on the DR potentials of customers.

The available main method for energy transition in the energy consumption side is the electric energy substitution. With respect to user-side energy transition, an energy co-pricing method for a park-level integrated energy system (IES) is addressed in this work. First, an energy co-pricing mechanism in the IES is introduced, with a park operator (PO) and user agents (UAs) included. A Stackelberg game model is next established with a PO and UAs involved, and a bi-level optimization formulation attained. The PO (the leader) sets prices of different kinds of energy products such as coal and electricity, with an objective to maximize its revenue of energy sales at the upper level, where penalty factors for non-electric energy consumption is added to the objective function of the PO. At the lower level, the UAs (the follower) could coordinate its coal-fired boiler and electric boiler to minimize its energy cost, given its heat demand satisfied. In this way, energy prices are attained and employed to provide the incentive for UAs to participate in electric energy substitution. The developed bi-level optimization problem is then transformed into a mixed integer liner programming problem with the Karush-Kuhn-Tucker optimality condition and duality theory employed. Finally, case studies of a park-level IES with the electric load demand, residential heat load demand and industrial heat load demand included are carried out to demonstrate the proposed model.

Nowadays, power system resilience presents higher requirements for restorative self-healing strategies considering both restoring and operating performances. This paper sheds light on a resilience-enhancing restoration strategy considering voltage balance, which is formulated as a two-stage mixed integer linear programming (MILP) problem. An improved non-black-start (NBS) generator start-up model is presented in the first stage, and used to determine the restoration sequence of generators, power lines, and loads with maximum restored load capacity. Besides, a linear power flow model with transmission loss ignored is employed in the second stage, with an aim of attaining most balanced post-restoration voltage by readjusting the power outputs from generators. The IEEE 30-bus power system is used to demonstrate the correctness and effectiveness of the proposed two-stage strategy.

The data center (DC) is a popular candidate for energy management due to its huge energy consumption and great flexibility potentials. In this paper, the energy management for an integrated energy system with loads of DCs (IESD) is addressed, focusing on energy-saving and carbonreduction. First, the loads of DCs are introduced as integrations of electric and cooling loads, and the IESD is modelled based on a combined cooling, heating, and power (CCHP) system. After that, a low-carbon economic dispatch model is formulated to minimize the operation cost including energy supplying cost and carbon trading cost, taking the unique demand characteristics of loads of DCs into account. Finally, case studies on a six-hub IESD are carried out. Simulation results demonstrate that the proposed method is able to promote the IESD operation with a more economic and low-carbon way.

Intelligent electronic devices represented by fault indicators (FIs) and advanced measurement infrastructures (AMIs) are widely installed by industrial and residential users in a distribution system concerned, and are new information sources for a power distribution dispatching center in dealing with fault diagnosis related issues. However, unreliable measurements of either the FIs on a feeder or the AMIs on the low-voltage side of a step-down transformer are inevitable, rendering some difficulties to accurate fault diagnosis. Given this background, an analytic model for fault diagnosis in a power distribution system is proposed considering unreliable alarms generated by FIs and AMIs. Characteristics of both kinds of measurement devices are formulated mathematically, and an optimization model for fault diagnosis is then attained. Finally, a sample power distribution system is employed to demonstrate the capability of the proposed model in accommodating unreliable alarms.

Power system resilience requires highly effective self-healing restoration strategies, and calls for superior performance both in system restoration and operation. Most existing restoration...

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this chapter, a wide-area measurement system (WAMS) and agglomerative hierarchical clustering (AHC) algorithm-based coherency identification method is presented for interconnected power systems with aggregated renewable sources. First, the trajectories measured by WAMS are transformed to the center of inertia (COI) based ones for better representing the dynamic behavior of a given power system, and ten dissimilarity indexes are presented for determining the similarity between the trajectories of any two generators. Second, a CRITIC (CRiteria Importance Through Intercriteria Correlation)-based method, in which entropy and the Spearman's rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to integrate the trajectory dissimilarity indexes. Next, the AHC algorithm is utilized to identify CGs. Finally, a modified New England-New York interconnected power system (NENYPS) with a large number of renewables, a simplified actual Western Interconnection power system (WECCPS) in North America, and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilized to demonstrate the proposed wide-area coherency identification methodology.

Although electric vehicles (EVs) can help alleviate the energy crisis and environmental pollution, their uncoordinated charging schedules could bring significant negative impacts on the secure and economic operation of power and traffic systems. Given this background, a decarbonization dispatching strategy for EVs based on life cycle analysis is proposed. First, life cycle analysis theory is briefly described. A microscopic traffic flow model (MTFM) is next established as a link between the power system and traffic system concerned. Further, according to the difference between urgent and flexible charging characteristics, a corresponding dispatching strategy is formulated for EVs. For fast charging EV users, the charging station (CS) selection and navigation strategy is addressed. For EV users employing slow charging, a bi-level optimal dispatching model considering decarbonization, distribution system security and battery loss is presented based on life cycle analysis. Finally, a coupled system including a 195-node traffic system and a 22-node distribution system is employed to demonstrate the effectiveness of the proposed model and method.

With the ever-growing complexity of a modern power system, decentralized control is becoming a more advantageous framework for maintaining its stable operation. Recently, some research work has been done on decentralized control for mitigating inter-area oscillations based on dynamic state estimation. This paper illustrates that in the decentralized control scheme, the use of dynamic state estimation can compromise controller performance and decrease the damping of inter-area oscillations. To address this problem, this paper proposes a cooperative-based supplementary excitation control strategy for damping inter-area oscillations using dynamic state information. By eigenvalue analysis, this paper reveals the relationship between the estimator's parameter and inter-area modes. Furthermore, communication is utilized in the proposed controller to neutralize the adverse impact of using dynamic state estimation on damping performance. The connection between the communication structure and inter-area modes is also analytically given. Finally, the well-known New England test system with 10 generators and 39 buses is employed to demonstrate the analytical results.

At present, electricity transaction in China is mainly conducted by medium- and long-term market contracts, while the spot electricity market has not yet been implemented. An appropriate energy imbalance processing mechanism for medium- and long-term power transactions is highly demanded for maintaining the stability of market operation, since it is supposed to guide market participants to reduce system imbalances and actively provide balancing services. Given this background, a bi-level optimization model for developing optimal bidding strategies of power producer in the monthly sequential contract and balancing markets is proposed based on the currently implemented monthly pre-listing balancing mechanism in China. In the upper level of the model, the monthly utility which makes a tradeoff between revenues and risks produced by the bidding strategy of the producer in the monthly sequential energy dual-market is maximized, considering the uncertainties of the monthly energy imbalances in the power system and the bidding strategies of rivals. In the lower level, the dispatch cost is minimized by simulating the three-stage process of sequential system operation, which includes contract market clearing by power exchange (PX), energy decomposition and modification by the system operator (SO) and balancing market clearing by PX. The simulation results of an eastern provincial electricity market in China show that the presented model can assist power producers in adjusting their monthly bidding strategies with relevant factors, such as the minimum output level of each producer, risk preference, and forecast information including system energy imbalance status and bidding strategies of rivals. In addition, the proposed method is also helpful for market designers in setting market parameters, such as government-authorized contract energy proportion and settlement thresholds in the balancing market.

Before the establishment of the spot electricity market in China, an energy imbalance settlement (EIS) mechanism has been proposed as a transitional solution to address the imbalances between actual consumption and contracted energy in the forward electricity market. It plays a crucial role in cultivating electricity retailers, maintaining the stability of the balancing account, and promoting a smooth transition to market-oriented reforms. Given this background, a novel EIS mechanism with a piecewise linear penalty pricing scheme is proposed, learning from the performance-based regulation (PBR) in distribution systems. For optimizing the parameters in the proposed EIS mechanism, a stochastic bilevel model is presented considering two kinds of stakeholders involved, namely the power exchange (PX) and retailers. In the upper-level model, an optimal parameter setting model for policymakers to minimize the variance of deposit in the balancing account of PX is presented. In the lower-level model, a decision-making tool for retailers under the renewable portfolio standard (RPS) is developed. As a self-balance measure of retailers, flexible demands are incorporated into the lower-level problem. Consumer psychology is applied to quantify customer consumption adjustments in response to the financial incentives given by retailers. The risk faced by the retailers is modeled using conditional value-at-risk (CVaR), taking into account the uncertainties associated with renewable energy production and customer demand. Simulation results of a provincial electricity market in China show that the proposed method can effectively motivate retailers to improve their imbalance management capability and assist policymakers in determining the parameters of the EIS mechanism. Besides, the proposed method provides insights into the impacts of parameter setting of the EIS mechanism on the behavior and performance of retailers.

In this paper, bushing-breakdown was identified as one of the main causes of transformer failure that resulted in a fire outbreak in July 2015 at the Gadong Power Station, Brunei Darussalam. To perform the investigation, three specimens from the burnt pieces were collected, which were white ash, bushing housing ashes and solid remnants from the burnt site. Both JSM-610F FIELD Emission Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) were used to obtain the electron images, and to analyze the types of elements present in the specimens. In addition, software simulations for the high voltage bushing failure were carried out using COMSOL Multiphysics Software for different oil levels, and the obtained results have been summarized. Finally, it is comprehended that fire outbreak happened due to leaking of the insulating oil in the high voltage bushing and loose tighten of the sealing part of the cable termination boxes.

In recent years, micro combined cooling, heating and power generation (mCCHP) systems have attracted much attention in the energy demand side sector. The input energy of a mCCHP system is natural gas, while the outputs include heating, cooling and electricity energy. The mCCHP system is deemed as a possible solution for households with multiple energy demands. Given this background, a mCCHP based comprehensive energy solution for households is proposed in this paper. First, the mathematical model of a home energy hub (HEH) is presented to describe the inputs, outputs, conversion and consumption process of multiple energies in households. Then, electrical loads and thermal demands are classified and modeled in detail, and the coordination and complementation between electricity and natural gas are studied. Afterwards, the concept of thermal comfort is introduced and a robust optimization model for HEH is developed considering electricity price uncertainties. Finally, a household using a mCCHP as the energy conversion device is studied. The simulation results show that the comprehensive energy solution proposed in this work can realize multiple kinds of energy supplies for households with the minimized total energy cost.

In this study, a multi-stage long-term expansion planning model for an active distribution network (ADN) is presented, with the aim of minimising the investment and operation cost in a coordinated manner over an established horizon. The planning model optimises the following alternatives: upgrading the capacities of substations, reinforcing and/or constructing cable circuits, placing voltage regulators (VRs) and/or static VAR generators, and determining the connection points for distributed generators (DGs). The investment decisions are optimised over the entire planning horizon which can be further divided into multiple periods, and the operation strategies, e.g. active management of DG as well as ADN topology reconfiguration, are determined according to the profiles of representative scenarios. To relieve the computational burden, the original model is properly simplified as a mixed-integer quadratic constrained programming problem through linearisation and approximation techniques, and the solution optimality is guaranteed after invoking the off-the-shell solver. A 24-node test system is employed to validate the effectiveness of the proposed model.

A scenario-based stochastic decision-making model is proposed in this paper to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets. Using the proposed strategy, the coordination of NGG and P2G facilities will provide a higher market payoff than that of independent NGG and P2G participation. The market price uncertainty is simulated in multiple scenarios using the Latin hypercube sampling method and the conditional value-at-risk strategy is adopted for evaluating the financial risks introduced by price uncertainties. The optimal bidding strategy is developed for both P2G and NGG operations and the Shapley-value method is employed to allocate the market payoff among NGG and P2G facilities. A case study which is based on the Pennsylvania, New Jersey, and Maryland market data is employed to verify the effectiveness of the proposed model and examine the characteristics of the proposed bidding strategy for the optimal operation of integrated NGG and P2G facilities.

With increasing utilizations of distributed energy resources and smart energy apparatuses, power distribution systems are undergoing a transition from passive to active networks, which challenges distribution system operators in many aspects of energy management and system control. In this paper, a short-term control strategy for active power distribution systems is proposed to regulate voltages into statutory ranges. The voltage control strategy is modeled as a convex optimization problem by applying a second-order cone relaxation. After the network decomposition, the alternating direction method of multipliers is employed to solve the established optimization model in a decentralized mode. Finally, the IEEE 33-bus and 123-bus test systems are employed to demonstrate the performance of the proposed voltage control strategy in active power distribution systems.

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality.

With the continuous development of smart grid and further restructuring of power industry, modern power systems have been transformed to complex cyber-physical systems characterized with high renewable energy penetrations, distributed facilities, advanced metering, and communication technologies, as well as ever-increasing customer awareness. These new development trends pose significant challenges for electricity retailers and call for innovative decision-making methods. To help researchers and engineers have a better overview of the state-of-the-art on electricity retail decision-making schemes, this paper aims to survey the latest progress on this subject. Some critical and open issues in this field are also discussed.

One of the major functions of a smart substation (SS) is to restore power supply to interrupted customers as quickly as possible after an outage. The high cost of a smart substation limits its widespread utilization. In this paper, a smart substation allocation model (SSAM) to determine the optimal number and allocation of smart substations in a given distribution system is presented, with the upgrade costs of substations and the interruption costs of customers taken into account. Besides, the reliability criterion is also properly considered in the model. By linearization strategies, the SSAM is simplified into a mixed integer linear programming problem which could be solved efficiently with commercial solvers. Finally, the performance of the proposed methodology is demonstrated by the standard RBTS-BUS 4 test system and a medium voltage power distribution system in Denmark.

The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviors. This study is based on the following assumptions: 1) Each retailer purchases electricity from the forward contract market, day-ahead spot market, and real-time market; 2) the competition among retailers is modeled by a market share function; 3) each retailer adopts fixed time-of-use prices for end-users; 4) the price fluctuations in day-ahead and real-time spot markets as well as uncertainty of electricity consumption behaviors are considered as main sources of risk. Under these assumptions, a pricing framework for retailers is established based on the bilevel programming framework and the optimal clustering in a time sequence. Meanwhile, profit risk is considered by taking conditional value at risk as the risk measure. The proposed bilevel optimization model is finally reformulated into a mixed-integer nonlinear programming problem by solving Karush–Kuhn–Tucker conditions. The online optimization solvers provided by the network-enabled optimization system server and the commercial solver AMPL/GUROBI are used to solve the developed models, respectively. Finally, a case study is employed to demonstrate the feasibility and efficiency of the developed models and algorithms.

The variability and uncertainty of renewable energy resources introduce significant challenges to power system operation. One particular example is the occurrence of ramp capability shortage in real-time dispatch, which can cause power balance violations and price spikes. To meet the increasing need for ramp capability, some independent system operators in the USA have led initiatives to promote the implementation of flexible ramping product (FRP). More potential FRP providers, apart from conventional generators, are being explored, among which battery energy storage (BES) appears to be a feasible option owing to its good controllability and fast responsive characteristics. This study proposes an optimisation model for a BES aggregator to optimally provide FRP in day-ahead energy and reserve markets, aiming to maximise its monetary benefits. The basic concept of FRP is first introduced, including comparisons with traditional ancillary services, pricing mechanisms, and the extensions of market models to integrate FRP. The modes and strategies for BES aggregators to participate in the electricity markets are then addressed. Case studies indicate that an aggregator can gain more profit by optimally allocating its resources among various products than only providing energy and reserves. A sensitivity analysis on several key factors is also conducted.

The wide-area measurement system provides a new data acquisition and supervisory control tool for a power system, and the data acquisition level is increased dramatically with its development in the smart grid environment. Huge data associated with the power system operation are acquired, which are beneficial for enhancing situational awareness of a power system concerned. Identifying the coherency among synchronous generators using real-time signals from phasor measurement units (PMUs) is one of the major tasks of situational awareness in power system operation. Given this background, a data-driven coherency identification methodology is proposed based on the spectral clustering algorithm. First, several trajectory dissimilarity indices for the rotor angle and rotor speed trajectories of generators as measured by PMUs are presented based on the trajectory similarity theory. Second, a decision-making method based on the Gini coefficient and Kendall rank correlation coefficient is presented for integrating multiple indices describing trajectory dissimilarities. Third, the spectral clustering algorithm is presented to identify the coherency of synchronous generators, and silhouette is presented for determining a reasonable number of coherent groups. Finally, oscillation events happened/simulated in two actual power systems, i.e., Guangdong power system in China and Western Interconnection power system in North America, are utilized to demonstrate the effectiveness of the proposed data-driven coherency identification methodology.

Solar power is recognized as one of the most crucial energies that contribute to environment protection and decarbonization in the world. In this work, an integrated solar system with concentrating solar power (CSP) plants and solar prosumers who are equipped with photovoltaic and photo-thermal facilities thus able to consume and produce electric and thermal power is studied for facilitating solar energy utilization and improving operational flexibilities of both the CSP plants and solar prosumers. In the integrated system, the CSP plant not only generates electricity but it also serves the thermal load of the solar prosumer through a heat exchanger. In order to investigate the benefits from integrating the CSP plant with solar prosumer, a coordinated stochastic scheduling strategy for the integrated system in the day-ahead (DA) electricity market is proposed considering the thermal interactions between the CSP plant and the solar prosumer as well as the demand flexibilities of electric, thermal and cooling loads of the prosumer. The coordinated scheduling problem is formulated as a mixed integer linear programming model by detailed modelling of each component of the CSP plant and solar prosumer. Simulations and comparisons on various cases demonstrate that, compared with the individual operations, the integration of the CSP plant and solar prosumer can remarkably boost the expected and DA profits and significantly improve the operational flexibilities of the CSP plant and solar prosumer.

This paper presents a review on the sources of failures of transformer in the substation. Different investigations and test analyses have been conducted to identify the root causes of failure of the transformer in the power system, and to identify the preventive measures to avoid these breakdowns. The review work has been presented with the focus on bushing-failure, which has been identified as one of the major causes of transformer breakdown in different reported studies. In determining the lifespan of the insulation of the high voltage bushing, along with different factors, moisture effect has also been considered in presenting the degradation analysis. On-line measurement has been preferred over off-line measurements due to its benefits measuring the reliability and uninterrupted functioning of the future generation, transmission and distribution systems. Post fire-outbreak, external inspection of the damaged bushing along with analytical results, obtained with the aid of Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analysis, have also been included in this study. In the analysis, electric field and equipotential lines with different oil levels have been simulated using COMSOL Multiphysics software.

Seven centrality indexes are first presented for identifying important nodes with topological features of a complex network and electrical characteristics of a power system considered. Then, a criteria importance through intercriteria correlation-based multi-index decision-making method, in which entropy and the Spearman’s rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to comprehensively identify the importance degrees of nodes in a given power grid. Finally, the proposed indexes and method are applied to actual Guangdong power system in China, and the results are compared with those attained by single-attribute and multi-attribute evaluation methods.

Performance of busbar insulators is affected by the accumulation of pollutants on their surfaces. The combined action of both the chemical and natural dust pollutants accumulated on the surfaces of the insulators results in the likelihood of surface flashovers. In this paper, the pollution level in terms of equivalent salt deposit density (ESDD) and chemical analyses of a 12-kV generator busbar insulators are carried out to identify the impacts of pollutants presented on the insulators. It is found by actual measurements that the ESDD values of five types of busbar insulators are 0.569, 1.66, 1.61, 1.58, and $$2.44~\hbox {mg}/\hbox {cm}^{2}$$ , respectively, and all of these values are beyond the acceptable limits. New mathematical models are proposed in between ESDD and chemical anions/cations using a multivariable linear regression analysis. A positive correlation is found between ESDD and cations for the T1-type insulator, whereas for the T5-type insulator, it is found between ESDD and anions.

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this study, a wide-area measurement system (WAMS) and agglomerative hierarchical clustering (AHC) algorithm based coherency identification method is presented for interconnected power systems with aggregated renewable sources. First, the trajectories measured by WAMS are transformed to the centre of inertia based ones for better representing the dynamic behaviour of a given power system, and ten trajectory dissimilarity indexes are presented for determining the similarity of the trajectories of any two generators. Second, a CRITIC (CRiteria Importance Through Intercriteria Correlation) based method, in which entropy and the Spearman's rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to integrate the trajectory dissimilarity indexes. Next, the AHC algorithm is utilised to identify CGs. Finally, a modified New England–New York interconnected power system with a large number of renewables, a simplified actual Western Interconnection power system in North America and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilised to demonstrate the proposed wide-area coherency identification methodology.

For cross-regional transmission projects, the two-part transmission pricing mechanism is suggested so as to promote the sustainable development of cross-regional electricity trading. In the two-part transmission pricing mechanism, appropriately determining the capacity charging ratio (CCR) is an important issue not well solved. Given this background, a Z-number-based risk-minimised negotiation model is developed for a transmission company and a power purchaser to achieve an agreeable CCR under incomplete information. The uncertainty distribution of the future annual electricity transmission quantity is first estimated by the Z-number-based multiple Z-valuations; and then, the benefit and risk loss measured by the well-established conditional value at risk (CVaR) are analysed for the participating two parties. Subsequently, the negotiation model where each negotiator is to minimise its risk loss under a given lowest acceptable benefit constraint and the estimations of the opponent's risk tolerance and negotiation strategy is presented to determine the optimal offer. Finally, the ± 500 kV Xiluodu−Guangdong direct current (DC) transmission project in the southern region of China is employed to demonstrate the basic characteristics of the proposed model.

Identification of transmission sections has an important implication on investigating and controlling operational risks of the power system concerned. The increasing coupling between electricity and natural gas networks has made the operations of both networks more complicated. A new method for identifying transmission sections in a power system is proposed in this paper considering the impacts of a natural gas network concerned. First, an integrated system of power and natural gas networks is optimized subject to operating constraints in both power and natural gas networks. The shortest electrical distance and the power sensitivity index are jointly employed next to form a comprehensive clustering index, and then a partition-based approach is used to identify transmission sections of the power system. Finally, a sample integrated system is built by combining the modified IEEE 39-node power system and Belgium 20-node natural gas system and employed to demonstrate the feasibility of the proposed method.

Energy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m2 solar heater and 90 m2 of solar panel can meet these energy requirements., This paper estimates the energy consumptions in residential areas. The study applies Spline interpolation by using two methods: autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The results show that 21.74% and 78.26% of the total energy are in use for water heating and other applications. The analysis demonstrates that 9 m2 solar heater and 90 m2 of solar panel can meet these energy requirements.

In this paper, a new dynamic pricing scheme is proposed for demand response aggregators (DRAs) to maximize their profits. A Stackelberg game is established to model the interactions between a load serving entity (LSE) and DRAs, and a bi-level optimization formulation attained. In the upper-level problem, the energy procurement from the wholesale electricity market is determined for the LSE based on the electricity price forecast, while DRAs adjust their consumption patterns with respect to the dynamic pricing scheme. The proposed model is transformed into a mixed-integer, quadratically-constrained quadratic programming (MIQCQP) problem, and the corresponding Karush-Kuhn-Tucker (KKT) conditions are attained to represent the optimality of the lower-level problem. The complementary constraints in the KKT conditions are linearized using big-M reformulation. Finally, the effectiveness of the proposed approach is demonstrated by numerical examples.

Consensus control has recently been employed to regulate active power output of distributed generators (DGs) of a virtual power plant. However, such networked control systems, integrating information and wireless communication technologies, are subject to cyberattacks. This paper investigates the risk of jamming attacks on the coordination of DGs in a distribution network. A binary-valued stochastic process is employed to model packet losses caused by jamming attacks and nonmalicious communication failures. Using an attack rate to characterize the strategy of an attacker, the proposed method highlights the vulnerability of the cooperative control of DGs subject to cyberattacks. Furthermore, a probabilistic instability analysis provides the conditions under which jamming attacks can prevent active power outputs of DGs from reaching a consensus to track a dispatch command. Accordingly, this paper shows how an attacker might launch malicious attacks to impair consensus-based control of DGs so that energy companies can take precautions. Finally, the theoretical results are verified on the IEEE 34-node test feeder under two different attack strategies.

Stochastic disturbances caused by renewable energy systems (e.g., wind power and solar power) may deteriorate the transient stability problems in a power system. This paper proposes a stochastic nonlinear excitation controller for transient stability enhancement in a multimachine power system. The third-order model of generators is adopted. A new probabilistic stability criterion is presented where the magnitude of a stochastic disturbance is included explicitly. Then the whole power system is represented by a nonlinear stochastic differential equation set. The proposed excitation controller is implemented by leveraging the feature of a stochastic port-Hamiltonian system. Compared with the controller design methods based on the deterministic power system model, the proposed method can improve the system stability even in the presence of continuous stochastic disturbances on power injections. Besides, it is convenient to select the Hamiltonian function as the stochastic Lyapunov function in the proposed approach. Finally, the effectiveness of the proposed method is demonstrated by a two-area test system with a three-phase fault.

This paper proposes an optimal dispatch model of an integrated energy system (IES), with carbon trading and flexible loads (FLs) considered. First, the carbon trading is introduced into the dispatch model, taking into account the carbon emissions of the entire energy supply chain. Next, flexible loads are divided into three categories, i.e. curtailable, transferable and shiftable ones, which are respectively modeled in detail. On this basis, electric network constraints, natural gas network constraints, together with internal constraints of energy hubs (EHs) are integrated into the low-carbon economic dispatch model with an objective of minimizing the operation costs including the carbon trading cost, flexible loads dispatching cost and system energy cost. Finally, an integrated energy system including 9 energy hubs is employed to demonstrate the proposed model, together with detailed analysis about the impacts of the carbon trading price and FLs on the IES.

─ The energy management in the residential sector, as a basic unit of energy consumption, has received extensive attention in recent years. To address this issue, a residential energy management system (REMS) is proposed, with two objectives considered, i.e., minimizing the costs and maximizing comfort-awareness of end-users. In the framework of the REMS, the economic role of a power-to-gas based storage system (P2GSS) is examined under different operating scenarios. The problem is first formulated as a mixed integer nonlinear programming (MINLP) model and then solved by an iterative decomposition method. In the proposed method, each objective is regarded as an independent sub-problem and refined iteratively, until the deviation comes into an acceptable range. Simulation results of a sample system demonstrate the positive role of the P2GSS in scheduling REMS economically and the effectiveness of the proposed method.