Urban Power System Research Articles

Regulation capacity evaluation of large-scale residential air conditioners for improving flexibility of urban power systems

The residential air conditioners (RACs) are increasing rapidly in urban power systems and have been widely considered as good regulation resources for improving the system flexibility and resiliency. However, in practical power systems, it is difficult to comprehensively acquire millions of RACs’ operating data and buildings’ thermal data, which makes the available regulation capacity of RACs tricky to evaluate. To address this issue, this paper proposes a Gaussian Mixture Model (GMM)-based evaluation method by utilizing partial easily observable data. First, a control framework of large-scale RACs is developed to provide regulation services for the power system. Based on the thermal–electrical models of RACs and buildings, a quantification method of the available regulation capacities is proposed under the premise of guaranteeing all the users’ comfortable indoor temperatures. Considering the practical condition of insufficient data acquisition of large-scale heterogeneous RACs, a GMM-based evaluation method is designed to calculate the probability of semi-info RACs’ expected regulation capacities by sampling a small portion of full-info RACs’ characteristics. Moreover, the Expectation Maximization Algorithm and the Bayesian Information Criterion are employed to optimize the multi-dimensional parameters and the component number of the GMM, which significantly improve the evaluation accuracy with lower complexity. The proposed models and methods are verified in a demonstration project on demand response in China.

A Coupled Multifield Estimation for Temperature Rise in a Gas Insulated Bus Bar Using ANSYS

Gas Insulated Switchgear (GIS) is an integral part of the urban power systems, which have been developed to address the substantial need for making substations more compact and reliable. The gas insulated bus bar is a crucial component of the power equipment in the substations with voltages over 100kV. In general, the ampacity of the bus bar is constrained by the maximum operating temperature, which has to be predicted, according to the demands of the standard IEC 62271. Therefore, thermal analysis of gas insulated bus bar has to be performed in the design of GIS. However, the mechanisms of heat generation and transfer in the SF6 gas and air are so complicated that it is not a simple problem to predict the temperature rise. Joule’s heat due to current in the main conductor and heat due to induced eddy currents in the tank is calculated via Magnetic Analysis. The heat transfer to the tank and atmosphere is done by means of radiation and natural convection. Heat transfer coefficients are not constant and vary on many parameters such as model geometry and material constants, making them difficult to calculate and apply to the boundaries. By considering the factors stated above, Nusselt number is used to analytically compute the temperature dependent heat transfer coefficients on the boundaries. The proposed research work presents a coupled magnetic and thermal field analysis supported by finite element analysis of extra-high voltage GIS bus bar with the help of ANSYS Maxwell and ANSYS Fluent. The results of the coupled finite element approach accord well with the values derived analytically.

Physical-cyber-human framework-based resilience evaluation toward urban power system: Case study from China.

Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical-cyber-human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and publicutilities.

Priority-Driven Self-Optimizing Power Control Scheme for Interlinking Converters of Hybrid AC/DC Microgrid Clusters in Decentralized Manner

Hybrid ac/dc microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. Practically, in networked microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. In order to realize optimal power exchanges among microgrid clusters, this article proposes a decentralized self-optimizing power control scheme for interlinking converters (ILCs). First, a priority-driven optimal power exchange model of ILCs is built that fully considers the priorities and capacities in subgrids. The whole optimization objective is to minimize the total dc-voltage/ac-frequency state deviations of subgrids. Second, to realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced. Consequently, as each of ILCs only monitors the local ac-side frequency and dc-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience and robustness in terms of communication faults. Finally, the proposed method is validated by three cases in hardware-in-loop environment.

Time Delay of Wide Area Damping Control in Urban Power Grid: Model-Based Analysis and Data-Driven Compensation

Due to the rapid development of economies, large urban cities consume an increasing amount of energy and have a higher requirement for power quality. Voltage source converter based high voltage direct current (VSC-HVDC) is a promising device to transmit clean power from remote regions to urban power systems, while also providing wide area damping control (WADC) for frequency stabilization. However, the time-delay naturally existing in the VSC-HVDC system may degrade the performance of WADC and even result in instability. To address this issue, this paper develops a time-delay correction control strategy for VSC-HVDC damping control in urban power grids. First, a small signal model of WADC is built to analyze the negative impacts of time delay. Then, a data-driven approach is proposed to compensate for the inherent time delay in VSC-HVDC damping control. The extensive training data will be generated under various disturbances. After offline training, the long short-term memory network (LSTM) can be implemented online to predict the actual frequency deviation based on real-time measurements. Finally, the proposed method is validated through MATLAB-Simulink in a two-area four-machine system. The results indicate that the data-driven compensation has a strong generalization ability for random delay time constants and can improve the performance of WADC significantly.

Multi‐level refined power system operation mode analysis: A data‐driven approach

The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data-driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high-dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi-level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine-grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

Localization and assessment of breakage defect in cables based on time–frequency domain reflectometry

Cables are very important in urban power system due to the small occupied area and excellent electrical properties. However, the breakage defect caused by manufacturing errors and improper installation can lead to great loss to the power system. Thus, the localization and assessment of breakage defect is necessary. In this paper, a non-destructive method based on time–frequency​ domain reflectometry is utilized, and the position and degree of breakage defect can be obtained by the peaks of the localization curve. Besides, pseudo Wigner–Ville distribution is proposed to avoid the cross-term interference problem in time–frequency domain reflectometry. In experiments, breakage defects of different degrees were caused to the same position of the target cable. The defects are localized accurately with errors less than 1% of the entire cable length, and as the degree of damage increases, the peak value of the assessment curve at the defect becomes larger accordingly.

Optimization strategy of combined thermal-storage-photovoltaic economic operation considering deep peak load regulation demand

Due to the randomness and uncertainty of renewable energy output and the increasing capacity of its access to power system, the deep peak load regulation of power system has been greatly challenged. The application of energy storage unit is a measure to reduce the peak load regulation pressure of thermal power units. In this paper, a joint optimal scheduling model of photovoltaic, energy storage units and thermal power units is established. The impacts of energy storage system on operation economy and photovoltaic abandonment are studied. The comprehensive operation cost measuring the operation economy considers the fuel cost that changes with the power in a quadratic function relationship, the startup-shutdown cost that changes linearly according to the number of start-shut actions, the compensation cost of deep peak load regulation according to the power block where the output locates in and the photovoltaic abandonment cost. The optimal scheduling model takes the minimization of comprehensive cost as the objective function, and the operation constraints of photovoltaic, energy storage units and thermal power units as the feasible range. The rolling optimization strategy is applied to solve the multi-time scale results. A real prefecture-level urban power system with seventy-four thermal power units is taken as an example. Different amounts of energy storage units are set for analysis. In the case assumed in this paper, the results show that enough energy storage configuration can improve the operation economy and reduce the load loss and photovoltaic abandonment to almost zero.

Mobile-Energy-as-a-Service (MEaaS): Sustainable Electromobility via Integrated Energy–Transport–Urban Infrastructure

The transport sector is one of the leading contributors of anthropogenic climate change. Particularly, internal combustion engine (ICE) dominancy coupled with heavy private motor vehicle dependency are among the main issues that need to be addressed immediately to mitigate climate change and to avoid consequential catastrophes. As a potential solution to this issue, electric vehicle (EV) technology has been put forward and is expected to replace a sizable portion of ICE vehicles in the coming decades. Provided that the source of electricity is renewable energy resources, it is expected that the wider uptake of EVs will positively contribute to the efforts in climate change mitigation. Nonetheless, wider EV uptake also comes with important issues that could challenge urban power systems. This perspective paper advocates system-level thinking to pinpoint and address the undesired externalities of EVs on our power grids. Given that it is possible to mobilize EV batteries to act as a source of mobile-energy supporting the power grid and the paper coins, and conceptualize a novel concept of Mobile-Energy-as-a-Service (MEaaS) for system-wide integration of energy, transport, and urban infrastructures for sustainable electromobility in cities. The results of this perspective include a discussion around the issues of measuring optimal real-time power grid operability for MEaaS, transport, power, and urban engineering aspects of MEaaS, flexible incentive-based price mechanisms for MEaaS, gauging the public acceptability of MEaaS based on its desired attributes, and directions for prospective research.

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Based on the real-time environmental constraints in urban regional construction, this paper constructed a bi-level decentralized low-carbon optimal dispatching model of the urban regional integrated energy system (RIES), including the park integrated energy systems (IESs). In this model, a bi-level optimal allocation model of carbon emission constraints between the urban and the park is proposed for the first time. The upper urban will formulate the real-time carbon emission constraints based on real-time environmental monitoring, decomposing the historical carbon emissions to the lower park IESs; the lower park will meet the real-time carbon emission constraints during optimization. We through the upper urban with the lower park between the bi-level decentralized optimization to ensure that the objective function’s upper urban power, natural gas, and heat distribution network system is minimum total network loss. In addition, it is necessary to ensure the minimum operating cost of each park IESs and focus on how to meet the requirements of the overall environment of urban RIES. Furthermore, we study the influence of optimal allocation strategy of carbon emission constraints on network loss, and operating cost of urban RIES under different scenarios. Then, an improved analytical target cascading (ATC) method is applied to solve the bi-level decentralized optimal dispatching model of urban RIES. Finally, an example under three different scenarios is given to verify the superiority and effectiveness of the proposed model and the improved method.

Research and Analysis of Power Supply Load Forecasting and Self-Healing Control in Urban Rail Transit System

With the continuous expansion of urban scale and the continuous increase of population, the pressure of urban traffic is increasing, so it is necessary to improve urban rail transit and better promote urban development and construction. At this stage, urban rail transit as an important tool of urban public transport, with fast, energy-saving and other advantages, so it has become an important part of urban infrastructure. Stable power system is the basis to ensure the stable operation of urban rail transit. However, traction power supply and electric power technology are needed to provide technical support in the operation of urban rail transit. This paper analyzes the content of this aspect and has a certain understanding of the relevant technology through the analysis of the article. Urge the relevant staff to conduct in-depth research, in order to obtain more advanced technology. With the gradual improvement of the reliability and advanced nature of the power grid, the self-healing ability has become the core function of the advanced power grid system in the future. This paper analyzes and compares the research status and technical characteristics of self-healing technology of distribution network system and rail transit power supply system, and points out the deficiency of self-healing of rail transit power supply system at present; by combing the fault scenarios and self-healing requirements of rail transit power supply system, this paper preliminarily analyzes the realization method of self-healing system, and puts forward an overall architecture of self-healing system. This paper first explains the causes of urban rail transit power system faults, then explains the fault types of urban rail transit power system, and finally explains the ways to deal with the faults of urban rail transit power system. With the continuous acceleration of urban modernization in China, urban rail transit has become the main force of urban transportation, providing people with more convenient travel, but also has a very high travel efficiency. It is precisely because of the characteristics of urban rail transit itself that it has become the darling of urban development, and many cities have plans to build rail transit.

Load Forecasting Based on LSTM Neural Network and Applicable to Loads of \u201cReplacement of Coal with Electricity\u201d

With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

Optimal scheduling for power system peak load regulation considering short-time startup and shutdown operations of thermal power unit

To balance the peak–valley difference of the system load in electrical power systems, the peak load regulation problem has become a major barrier, resulting in challenges to unit commitment (UC). In addition to the new construction of specific system peak load regulation resources on the generation side and the demand response programs on the demand side, this paper focuses on the capacity of thermal power units in system peak load regulation. Considering the temporal distribution of system load off-peak hours, the potentiality of the deeper peak load regulation mode and the short-time startup and shutdown regulation mode of thermal power units are analysed. This paper presents an optimal scheduling model for power system peak load regulation considering the short-time startup and shutdown operations of a thermal power unit. First, an improved linear UC model considering the thermal units’ startup and shutdown trajectories is established. Next, for different peak load regulation modes of thermal units, the corresponding peak load compensation rules are processed and converted into linear formulations. An integrated optimal scheduling model for power system peak load regulation with a suitable rolling optimization strategy is proposed. A real prefecture-level urban power system in southwest China and its modified test systems are used to test and verify the validity and effectiveness of the proposed methodology.

SolarEV City concept: building the next urban power and mobility systems

Cities have become the focus of global climate mitigation efforts because as they are responsible for 60%–70% of energy-related CO2 emissions. As the world is increasingly urbanized, it is crucial to identify cost-effective pathways to decarbonize and enhance the resilience of cities, which ensure the well-being of their dwellers. Here, we propose a ‘SolarEV City’ concept, in which integrated systems of cities’ roof-top photovoltaics and electric vehicles (EVs) supply affordable and dispatchable CO2-free electricity to urban dwellers. Our analyses indicate that implementations of the concept can meet 53%–95% of electricity demands in nine major Japanese urban areas by 2030. CO2 emission from vehicle use and electricity generation in these areas can be reduced by 54%–95% with potential cost savings of 26%–41%. High cost-effectiveness and seasonally stable insolation in low latitudes may imply that the concept may be more effective to decarbonize urban environments in emerging economies in low latitudes. Among several factors, governmental interventions will play a crucial role in realizing such systems, particularly in legislating regulations that enhance penetration of the integrated system of PV and EV and enable formation of decentralized power systems. As bottom-up processes are critical, policy makers, communities, industries, and researchers should work together to build such systems overcoming social and regulatory barriers.

Reconfiguration Performance of the Urban Power Distribution System Based on the Genetic-Ant Colony Fusion Algorithm

This study aims to enhance the reliability of the urban power grid system and decrease the economic loss due to power network faults. Based on the analysis of the traditional algorithms for restructuring the urban distribution system after faults, this study proposes an upgraded genetic algorithm (GA) and ant colony algorithm (ACA) and combines these two to overcome the limitations of the local optimum of GAs and low convergence speed of ACAs. Taking the IEEE33-node system as the research object, the network loss, maximum recovery of the power-loss load, and the number of switching operations as the objective function, the impact of different algorithms on the restoration and reconfiguration of the distribution system was examined according to MATLAB system simulation and the optimal algorithm for the reconfiguration of the urban distribution system failure recovery. The experimental results revealed that compared with the current distribution system reconfiguration algorithm, the genetic-ant colony algorithm (GACA) has higher algorithm time efficiency and solution accuracy and can markedly decrease the recovery time and improve the impact of the distribution system in a short period. Overall, the proposed GACA is an efficient self-healing algorithm of urban distribution systems and useful for augmenting the reliability of the urban power system.

A review of regional distributed energy system planning and design

With the development of urbanisation in China, regional distributed energy plays a vital role in modern urban power systems, which can reduce emissions, reduce power consumption, and increase the safety and reliability of power grids. This paper focuses on three main aspects of regional distributed energy system planning and design: load forecasting, system optimisation and system evaluation. It summarises the types and research methods of each aspect and proposes the future development direction. Finally, the research framework of system planning and design is proposed.

Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis

Demand-side management provides important opportunities to integrate renewable sources and enhance the flexibility of urban power systems. With the continuous advancement of the smart grid and electricity market reform, the potential for residential consumers to participate in energy demand response is significantly enhanced. However, not enough is known about the public perception of energy demand response, and how sociopsychological and external factors could affect public willingness to participate. This study investigates the public perception of and willingness to participate in urban energy demand response through a questionnaire survey and employs multiple linear regression models to explore the determinants of public willingness to participate. The results suggest that income level, energy-saving attitudes, behaviors, external motivation factors, and energy-saving technologies are the key factors that determine public willingness to participate. Although most respondents are willing to participate, the effects of monetary incentives are more significant than the effect of spiritual inducements, and respondents are more sensitive to compensation than to dynamic electricity prices. The further improvement of residential responsiveness requires continuous infrastructure building by technical support, public energy-saving awareness, and public perception of energy demand response. Policy implications are proposed to achieve a sufficient residential response from an aggressive policy framework and energy-saving behavioral guidance.

Ensemble Learning for Load Forecasting

In this paper, an ensemble learning approach is proposed for load forecasting in urban power systems. The proposed framework consists of two levels of learners that integrate clustering, Long Short-Term Memory (LSTM), and a Fully Connected Cascade (FCC) neural network. Historical load data is first partitioned by a clustering algorithm to train multiple LSTM models in the level-one learner, and then the FCC model in the second level is used to fuse the multiple level-one models. A modified Levenberg-Marquardt (LM) algorithm is used to train the FCC model for fast and stable convergence. The proposed framework is tested with two public datasets for short-term and mid-term forecasting at the system, zone and client levels. The evaluation using real-world datasets demonstrates the superior performance of the proposed model over several state-of-the-art schemes. For the ISO-NE Dataset for Years 2010 and 2011, an average reduction in mean absolute percentage error (MAPE) of 10.17% and 11.67% are achieved over the four baseline schemes, respectively.

Capacity value estimation of plug-in electric vehicle parking-lots in urban power systems: A physical-social coupling perspective

This paper presents a study to investigate the potential role of public plug-in electric vehicle grid-connected parking lot (GPL) in improving the sustainability of power supply as virtual energy storage in the urban grid. For this, a comprehensive evaluation framework is proposed which introduces an extension of the generation-oriented concept of capacity value to GPL, with the aim of assessing their contribution to adequacy of supply for different settings. In order to capture the volatility in the available energy from GPL, a compound parametric model is developed from the perspective of physical-social system. Comparing with peer studies, our proposed approach has prominent advantages: (a) it accounts the effects from both technical and human factors associated with the GPL operations; (b) it considers the uncertainty of vehicle users’ preference for using GPL and its potential dynamics with the externalities. Hence, it can provide a more comprehensive and realistic representation of the operating characteristics of the GPL. A general framework based on the sequential Monte-Carlo simulation method is developed to derive the corresponding capacity value indices. The effectiveness of the proposed method is validated through implementation on both a standard test case and a real distribution system in China. The obtained results suggest that the capacity value of GPLs could vary significantly, dependent on both the operational characteristics of facilities and the charging pattern of electric vehicles. Additionally, the uncertainties in drivers’ behaviors can have a noticeable impact on the reliability benefits of GPL program.

Analysis about Power Supply Capability, Economy and Reliability on Voltage Level Sequence of Urban DC Distribution Power System in Integrated Energy System

With the development of DC power transmission technology, it is possible to develop a multilevel urban DC distribution system in integrated energy system. With the basic principle and main constraints of designing for DC distribution voltage level sequence, a sequence of voltage levels of DC distribution power system in different voltage levels is proposed, and the power supply capability, economy evaluation and reliability on voltage level sequence of multilevel DC distribution is discussed in detail through a specific example.