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Abstract There is growing consensus that gas-fired generators will play a crucial role during the transition to net-zero energy systems, both as an alternative to coal-fired generators and as a flexibility service provider for power systems. However, malfunctions of gas networks have caused several large-scale power blackouts. The transition from coal and oil to gas fuels significantly increases the interdependence between gas networks and electric power systems, raising the risks of more frequent and widespread power blackouts due to the malfunction of gas networks. In a coupled gas–electricity system, the identification and transmission of gas network malfunction information, followed by the redispatch of electric power generation, occur notably faster than the propagation and escalation of the malfunction itself, e.g., significantly diminished pressure. On this basis, we propose a gas-electric early warning system that can reduce the negative impacts of gas network malfunctions on the power system. A proactive control strategy of the power system is also formulated based on the early warning indicators. The effectiveness of this method is demonstrated via case studies of a real coupled gas–electricity system in China.

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.

High-quality demand-side management requires an abundance of load profiles to support decision-making processes. However, customer energy consumption data often contains sensitive personal information, and service providers face significant challenges in accessing a substantial amount of energy consumption data. To generate a large volume of customer data without compromising privacy, this study introduces a data-driven approach integrating Information Maximizing Generative Adversarial Networks (InfoGAN) with Multivariate Kernel Density Estimation (MKDE) for the generation of load profiles. InfoGAN is firstly trained based on existing customer load profiles, with the Q network disentangling the load into feature variables and the generator producing realistic profiles. Subsequently, MKDE is utilized to assess the distribution of these features, enabling the generation of new profiles by sampling new feature variables. The proposed method circumvents the need for intricate sampling or modeling processes and generates realistic data that represents the inherent uncertainties and fluctuations characterizing customers’ electricity consumption. The generated data could be used as the substitution for real electricity consumption data, thereby facilitating further applications without compromising privacy concerns.

With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and fully exploiting data asset value in a trustworthy environment. Here, we focus on a global aspiration and interest regarding the challenges, techniques, and prospects of data sharing in energy systems. In this paper, a conceptual framework for data sharing is designed, in which we introduce the commodity attribute of data assets and explain the bottlenecks of data trading. Two critical issues, i.e., right confirmation and privacy protection, are then systematically reviewed, which provide a fundamental guarantee for credible data openness. A detailed data market is conceived by elaborating on market bids, data asset valuation and pricing strategy, and game-based clearing. Finally, we conduct a discussion about some low-hanging fruit of data sharing in energy systems.

Distributed integrated multi-energy systems (DIMSs) can be regarded as virtual power plants to provide additional flexibility to the power system. This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region. The aggregation model includes the power constraints, energy constraints, and ramping constraints to aggregate different types of resources in the DIMSs. The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system. A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem, which is more efficient than the KKT-based algorithms. Finally, a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model.

State estimation (SE) usually serves as the basic function of the energy management system (EMS). In this paper, the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established. Then, a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline. Meanwhile, to accommodate the measuring resolutions of the integrated network, a hybrid SE approach is developed based on the two-stage iterative algorithm. Results show that, in both steady and dynamic processes, the two-stage estimator has good accuracy and convergence. The hybrid estimator has good performance on tracking the variation of the states in the heating network, even when the available measurements are limited.

Coupling between electricity systems and heating systems are becoming stronger, leading to more flexible and more complex interactions between these systems. The operation of integrated energy systems is greatly affected, especially when security is concerned. Steady-state analysis methods have been widely studied in recent research, which is far from enough when the slow thermal dynamics of heating networks are introduced. Therefore, an integrated quasi-dynamic model of integrated electricity and heating systems is developed. The model combines a heating network dynamic thermal model and the sequential steady-state models of electricity networks, coupling components, and heating network hydraulics. Based on this model, a simulation method is proposed and quasi-dynamic interactions between electricity systems and heating systems are quantified with the highlights of transport delay. Then the quasi-dynamic interactions were applied using security control to relieve congestion in electricity systems. Results show that both the transport delay and control strategies have significant influences on the quasi-dynamic interactions.

Storage is widely considered in economic dispatch (ED) problems. To prevent simultaneous charging and discharging of a storage device, a storage-concerned ED problem should involve complementarity constraints for every storage device to make the problem strongly non-convex. In this case, the conventional Karush-Kuhn-Tucker optimality conditions are unsuitable, and the methods that are normally effective are also invalid. In our recent paper, we proposed a new exact relaxation method that directly removes the complementarity constraints from a storage-concerned ED model to make it convex and easy to solve. This paper extends the previous study by presenting and analyzing two new groups of sufficient conditions that guarantee exact relaxation. Different application conditions of these groups of sufficient conditions are discussed. Numerical tests are performed to show the benefit of using the exact relaxation method and the different suitable application conditions of these groups of sufficient conditions. This paper contributes to a wide application of exact relaxation in storage-concerned ED problems.

The increasing deployment of fast charging infrastructure is coupling the operation of power and transportation systems. However, how to evaluate the coupling relationship between these two systems is less studied. This study proposes a look-ahead decentralised framework to solve the integrated power-traffic flow problem using the optimality condition decomposition (OCD) technique. By exploring the similarity between the iterative procedure of the algorithm and the interactive decision-making process of the systems, this study adds two original contributions to this literature. First, the authors show that the convergence performance of the OCD algorithms is closely related to the interdependency between the power and transportation systems. Numerical oscillations or failure of convergence could occur in highly interdependent systems. This can be used as a metric for assessing whether the coordinated operation of the two systems is essential. Second, they employ a dynamic multiplier-based OCD algorithm to solve the integrated flow problem in a decentralised manner with improved convergence performance. Their proposed algorithm considers the sensitivity of the electricity prices to the charging demand, which reduces the price fluctuations at some congested electrical nodes to enhance the convergence speed. The case study demonstrates factors that influence the coupling relationship between the power and transportation systems.

Abstract Power systems depend on discrete devices, such as shunt capacitors/reactors and on-load tap changers, for their long-term reliability. In transmission systems that contain large wind farms, we must take into account the uncertainties in wind power generation when deciding when to operate these devices. In this paper, we describe a method to schedule the operation of these devices over the course of the following day. These schedules are designed to minimize wind-power generation curtailment, bus voltage violations, and dynamic reactive-power deviations, even under the worst possible conditions. Daily voltage-control decisions are initiated every 15 min using a dynamic optimization algorithm that predicts the state of the system over the next 4-hour period. For this, forecasts updated in real-time are employed, because they are more precise than forecasts for the day ahead. Day-ahead schedules are calculated using a two-stage robust mixed-integer optimization algorithm. The proposed control strategies were tested on a Chinese power network with wind power sources; the control performance was also validated numerically.

Energy management system (EMS) is one of the most essential categories of the advanced applications in current cyber-physical power systems. However, because of the tight coupling relationship between a power network and EMS's communication network, a physical break-line fault may be accompanied by a communication line outage, which may result in regional unobservability and uncontrollability. To maximally avoid and eliminate such operation risks, referring to current EMS's ‘active + standby’ communication configuration scheme in China, the authors propose a dynamic routing optimisation mechanism for its standby communication routings, assigning the most reliable communication lines to the most important information. Such optimisation considers two factors: information's significance and the reliability of communication lines. By introducing cyber-physical sensitivity index and path-branch incidence matrix, both factors can be expressed numerically. In the case study, the authors optimise the standby communication routings for a power flow corrective control application under different scenarios. The results verify the effectiveness and superiority of their approach.

This paper presents a fast coordinated control scheme of the rotor side converter (RSC), the Direct Current (DC) chopper and the grid side converter (GSC) of doubly fed induction generator (DFIG) wind turbine generators (WTGs) to improve the low voltage ride through (LVRT) and high voltage ride through (HVRT) capability of the DFIG WTGs. The characteristics of DFIG WTGs under voltage sags and swells were studied focusing on the DFIG WTG stator flux and rotor voltages during the transient periods of grid voltage changes. The protection schemes of the rotor crowbar circuit and the DC chopper circuit were proposed considering the characteristics of the DFIG WTGs during voltage changes. The fast coordinated control of RSC and GSC were developed based on the characteristic analysis in order to realize efficient LVRT and HVRT of the DFIG WTGs. The proposed fast coordinated control schemes were verified by time domain simulations using Matlab-Simulink.

When large wind farms are centrally integrated in a power grid, cascading tripping faults induced by voltage issues are becoming a great challenge. This paper therefore proposes a concept of static voltage security region to guarantee that the voltage will remain within operation limits under both base conditions and N-1 contingencies. For large wind farms, significant computational effort is required to calculate the exact boundary of the proposed security region. To reduce this computational burden and facilitate the overall analysis, the characteristics of the security region are first analyzed, and its boundary components are shown to be strictly convex. Approximate security regions are then proposed, which are formed by a set of linear cutting planes based on special operating points known as near points and inner points. The security region encompassed by cutting planes is a good approximation to the actual security region. The proposed procedures are demonstrated on a modified nine-bus system with two wind farms. The simulation confirmed that the cutting plane technique can provide a very good approximation to the actual security region.

The operation of power systems has been complicated by the rapid diversification of loads. Analyzing load characteristics becomes necessary to different utilities in energy management systems to ensure the reliability of power systems. Here, we describe a method of analyzing and quantifying the load characteristics and introduce its application to pilot nodes selection for zone based voltage control. We propose a new index, the Q-fluctuation (QF), to quantify the load characteristic of reactive power based on an analysis of historical data. A second index, the V-fluctuation (VF), which is a combination of the QF and the Q–V sensitivity that reflects structural information for the grid describes the voltage deviation at each node. These indices are used to construct the voltage fluctuation space, which is then used to select the pilot node for each zone. Simulation studies using IEEE 14-bus and 118-bus systems are described, and used to demonstrate the advantages of the proposed method. The method was able to improve the secondary voltage control and enhance the grid reliability in response to structural changes.

In Part I of this work, a static voltage security region was introduced to guarantee the safety of wind farm reactive power outputs under both base conditions and N-1 contingency. In this paper, a mathematical representation of the approximate N-1 security region has further studied to provide better coordination among wind farms and help prevent cascading tripping following a single wind farm trip. Besides, the influence of active power on the security region is studied. The proposed methods are demonstrated for N-1 contingency cases in a nine-bus system. The simulations verify that the N-1 security region is a small subset of the security region under base conditions. They also illustrate the fact that if the system is simply operated below the reactive power limits, without coordination among the wind farms, the static voltage is likely to exceed its limit. A two-step optimal adjustment strategy is introduced to shift insecure operating points into the security region under N-1 contingency. Through extensive numerical studies, the effectiveness of the proposed technique is confirmed.

Spinning reserve allocation is a critical problem for active power dispatch with large-scale wind power penetration. A risk-based reserve allocation method that accounts multiple control sub-area coordination is given in this paper. And a multi-objective optimization model is constructed to schedule the spinning generation reserve for online active power dispatch. A fuzzy optimization method is used to transform the multi-objective optimization problem into a single-objective optimization one. The relationship between loss of load expectation and spinning reserve is also derived. And a particle swarm optimization (PSO) method is employed to provide a numerical solution to the problem. Numerical tests on IEEE RTS system are also given to validate the proposed method.

Electric vehicles (EVs) and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU) policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably.