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Network and load recovery (NLR) during power system restoration is a multi-step mixed-integer nonlinear programming (MINP) problem. The NLR is difficult to be solved as it is NP-hard. Thus, NLR is commonly solved step by step, which is short-sighted and will result in longer restoration time. To obtain a far-sighted NLR plan, this paper proposes an improved two-stage optimization method for NLR. The first stage adopts a mixed-integer linear programming (MILP) model to obtain optimal solutions of integer variables in NLR, namely the load pick-up schedules and transmission line charging schedules. Then in the second stage, a continuous non-linear optimization method based on AC power flow with frequency constraints and load model is established to minimize the restoration duration of the plan generated in the first stage step by step. Case studies are undertaken on a 10-machine 39-bus system and Southeast Hubei Provincial power system of China. Simulation results indicate that the restoration plan obtained from the improved two-stage optimization method is highly effective, while the computational efficient meets the intensive need for restoration scheduling after blackouts.

The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future.

The permeability of renewable energy sources rapidly increases in the microgrid for economic and environmental concerns. However, the uncertainty of renewable energy may lead to frequency fluctuation or even instability in the microgrid. This paper proposes a dynamic event-triggered robust secondary frequency control scheme for the islanded microgrid to handle the uncertainty of the renewable energy source. In addition to the enhancement of frequency stability, the communication burden is also considered in this work. The proposed dynamic event-triggered communication scheme provides a good balance between dynamic performance and communication burden. Based on the discontinuous Lyapunov function, the stability analysis helps the optimal gain selecting. Given the designed controller, the derived theorems in this paper can help to examine whether the communication system fulfills the requirements from the controllers. To validate the proposed method, case studies are carried out based on two typical microgrids. The simulation results are compared with conventional frequency controllers. It shows that the proposed controller outperforms other types of controllers in term of the frequency response performance and communication burden.

This paper presents a security-constrained co-planning of transmission line expansion and energy storage with high penetration of wind power. The energy storage can not only improve the accommodation of renewable generation but also help to mitigate the emergency overflow under the post-contingency state. Also, the adjustment capabilities of the wind farms are considered, and the risk of excessive wind curtailment under the post-contingency state is assessed quantitatively. The coordinated multistage corrective control strategy for the battery storage, the pumped storage, thermal generators, and wind farms is studied. The mixed-integer linear programming problem based on the proposed corrective control strategy has been formulated. The Benders decomposition algorithm is introduced to divide the co-planning problem into one master problem and two sub-problems. In the master problem, the system is operating under normal conditions, so the security constraints are not considered. It minimizes the sum of the investment cost of newly built facilities and the operational cost. In the sub-problems, the corrective control during different post-contingency stages is formulated, minimizing the risk costs under fault conditions. The two-step technique is developed to generate the Benders cuts from the mixed-integer linear programming sub-problems, and return to the master problem. The real-world power system in Gansu, China is studied to validate the applicability and scalability of the proposed model and solution technique. The results denote that the co-planning model with security constraints is profitable in the real power system, with savings of 25% for the newly built transmission lines and a 5.5% reduction in total cost.

Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments.

In this work, a real-time subsidy based robust scheduling method for the integrated gas and power system is proposed. Bi-directional energy conversion, including the power-to-gas and gas-fired generation, is operated via dynamic variant price signals. The gaming between the electrical power system and the natural gas system is formulated in the bi-level optimization. The upper-level problem minimizes the operational cost, in which the real-time subsidy for power-to-gas and gas-fired units is obtained. The lower-level problem maximizes the profit for the power-to-gas and gas-fired units, in which the transient gas flow is introduced. In addition, in order to counter the uncertainties brought on by wind power, a real-time subsidy update strategy based on robust optimization is proposed to stimulate the regulation capabilities of the power-to-gas and gas-fired units. This bi-level optimization is reformulated as a mixed-integer quadratic programming problem using the Karush-Kuhn-Tucker optimization conditions. Simulation results show that the real-time subsidy scheduling can make power-to-gas and gas-fired units follow the system operator’s preferences such as wind power accommodation, mitigation of unsupplied load, and relieving network congestion.

Integrating power and heat sectors can increase the flexibility of wind power integration. Power-to-heat devices powered by renewables can promote the electrification and decarbonization of the heat sector. This paper explores the feasibility and benefits of using large-scale heat pumps for reserve provision. A two-stage stochastic optimization model is proposed for the day-ahead centralized scheduling of the integrated electricity and heat system considering wind power uncertainties, with reserve provision of large-scale heat pumps, combined heat and power units, and thermal power units. The model co-optimizes energy, following reserve, regulating reserve, and heat regulation to handle wind power uncertainties. To reduce the computational complexity of the scenario-based stochastic optimization problem, inactive transmission line constraints are identified and removed based on an analytical calculation method, which considers the influence of power consumption of large-scale heat pumps on the power flow of transmission lines. The benefits of using reserves from large-scale heat pumps are tested on the modified IEEE-118 bus integrated electricity and heat system. The simulation results show that using large-scale heat pumps to provide the following reserve and regulating reserve show similar impacts on saving cost and reducing wind energy curtailment, and the acceleration algorithm can save computation time by approximately 64%.

Large-scale integrated energy systems often encompass several subsystems divided by geographic areas. Coordinated operation of multi-area integrated energy systems can enhance operational flexibility, which is crucial for systems with high wind power penetration. Traditional centralized methods have some inherent defects, such as privacy protection, communication burden, and computational capability, etc. To address such challenges, this paper proposes a decentralized computation method for the robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power. The interconnected multiple areas can cooperate for improved system flexibility and wind power integration capability. Using the decentralized method, the area operator optimizes its operation decisions independently without transferring its dispatch rights. The shared information is limited to exchanged energy flows. The proprietary information is thus preserved while the communication burden is significantly reduced. To solve the robust operation problem distributedly, the decentralized method is enhanced to cope with both deterministic and robust problems. A tri-level algorithm composed of the iterative alternating direction multiplier method and the column constraints generation method is utilized to realize the decentralized optimization. Numerical results for three test systems show the effectiveness, computational quality, and scalability of the proposed method. Compared to the isolated method, the proposed method fully utilizes the coordination effect of multi-area RD-IES and has obvious economic advantages, especially in scenarios with high wind power penetration levels. The proposed method brings a 12.4% cost reduction in the two-area system and a 22.4% cost reduction in the eight-area system.

Chronological operation simulation (COS) is an essential tool for planning and analyzing power systems under high penetration of renewable energy. Conventional COS methods heavily depend on the availability of renewable power output data to obtain accurate results, and often require hours or even days of computational time while the sequential simulation could easily get infeasible for power systems with intensive flexibility. To cover the absence of output data for newly proposed wind and solar projects and accelerate the computation speed, this paper proposes a novel COS simulation framework for regional power systems with high penetration of renewable energies using meteorological data. The proposed simulation framework consists of the following three steps: data preparation, modeling and solving, and result output. In the data preparation step, wind, solar power output profiles and heat demands are converted from public accessible meteorological data. Then in the modeling and solving step, a unit commitment based COS model for simulating the hourly operation of power and heat sectors is proposed, and the proposed model is solved with a time domain partitioning (TDP) and a rollback mechanism to accelerate the computation speed as well as avoiding infeasible solutions. The accuracy of the wind and solar power output converted from meteorological data is verified through comparing with measured power output. Moreover, the feasibility and accuracy of utilizing the proposed COS framework to simulate the operation of a real regional power system is also verified through the 2015 annual operation statistics of the Northwest China Grid.

Energy hub (EH) has attracted worldwide appeals and shows broad prospects in promoting high-level renewable energy integration as well as supplying various loads with high flexibility. Although the cooperation of multiple EHs will further improve the flexibility and economy, how to allocate the payoff fairly and convincingly remains challenged. To address the above challenge, this paper provides a market mechanism based on EH’s peer-to-peer (P2P) transaction for the operation of multiple EHs. The cooperative game theory is applied to develop a fair and convincing payoff allocation scheme. This paper further proves that the cooperative game of multiple EH operations is a balanced game, demonstrating EHs’ grand coalition's stability. The payoff allocation problem is formulated as a two-stage problem. In the first stage, payoff allocation is determined to minimize the worst-case excess. In the second stage, the coalition with the highest excess is found and returned to the first-stage problem. To solve the two-stage problem, the solution technique utilizing column constraints generation is provided. Numerical results for two test systems show the effectiveness, computation performance, and scalability of the proposed method. In the test system, each EH has its payoff increased, ranging from 4% to 19%. The proposed solution technique avoids the computational burden growing exponentially and computes 4168 s when the number of possible coalitions is up to 4.29e9.

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatio-temporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID-19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties.

Using the improved flexibility from the district heating system with multiple flexible devices is an effective solution to ensure the cost-effective and secure operation of the integrated energy system with high penetration of renewables. This paper exploits the improved flexibility of the integrated electricity and heat systems by using reserves from multiple flexible devices to accommodate more wind power and reduce operational costs. A two-stage stochastic optimal dispatching scheme is proposed for the integrated electricity and heat system considering both power networks and heat pipelines, and reserves from the condensing combined heat and power units, heat pumps, electric boilers, and heat storage tanks. The proposed scheme balances the power and heat sectors both in the day-ahead and real-time stages with the synergy of different flexible devices and linkage for each device in the two stages. A scenario generation method considering spatial-temporal correlation is proposed to provide reasonable wind power profiles for the two-stage dispatch scheme. The Gaussian mixture model and exponential function are used to construct the spatial and temporal correlation, respectively, and the Gibbs sampling is utilized to reduce the sampling complexity. The case studies were conducted on a 6-bus integrated electricity and heat system and a practical integrated energy system in Northern China. The results show that utilizing the scenario set with spatial-temporal correlation and improved flexibility can effectively reduce the operational cost and wind power curtailment.

Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing of car journeys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO2 is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers.

Online transient stability assessment (TSA) is vital for power system control as it provides the basis for operators to decide emergency control actions. But none of previous TSA research has taken into consideration the difference between two instability modes (aperiodic instability and oscillatory instability), which may threaten secure operation of power system. To address this problem, a TSA and instability mode prediction method based on convolutional neural network is proposed. The method takes the bus voltage phasor sampled by phasor measurement units (PMUs) during a short observation window after disturbance as input, and outputs the prediction result promptly: stable, aperiodic unstable or oscillatory unstable. The end-to-end model automatically extracts needed features from the raw measurement data, thus freeing itself from reliance on expertise. At the offline training stage, stochastic gradient descent with warm restart (SGDR) optimization algorithm is employed so that the model tends to converge to 'flat' and 'wide' minima with better generalization ability. Case studies conducted on New England 39-bus system and Western Electricity Coordinating Council (WECC) 179-bus system demonstrate superior accuracy, adaptability and scalability of the proposed method compared with conventional machine learning methods. Furthermore, the proposed model is empirically proven to be robust to PMU noise and loss.