Energy Conversion and Economics最新文献

With the increasing penetration of renewable energy, a new type of energy system, transactive energy systems (TES), has emerged. This study investigates the challenges of optimally operating a TES distribution system with demand response (DR) from the cyber-physical-social system (CPSS) perspective. A TES optimization framework that integrates artificial systems, computational experiments, and parallel energy theory for modelling DR, via parallel system theory, is introduced. A data-driven artificial DR system is created and modelled using limited data. In the computational experiment, a complete information Stackelberg game model for the distribution network operator and the artificial DR system is built. This simulates the response relationship between the distribution network operator and the electricity consumer under different price conditions. In the parallel energy optimization model, a multi-time scale energy optimization method which considers day-ahead and intraday scenarios, the interaction between the actual TES and the artificial DR system is shown. Finally, empirical data from the Henan province in China is used as a case study to verify the effectiveness of the optimization method proposed in this study.

Voltage sags flickers, and harmonics are major problems associated with power quality and are of concern to power companies and consumers. Therefore, efforts to improve power quality have increased. This study explores the dynamic performance of a static synchronous compensator (STATCOM), which is used to mitigate voltage sags, flickers, and harmonics using a novel control method. Further, the ability of the STATCOM system to alleviate issues related to power quality while, in addition, improving the transmission/distribution system performance concerning all disturbances and errors related to the system is validated. The methodology of this research adopts MATLAB/SIMULINK to perform the requested simulations, which were analysed accordingly. The results show an improvement in the system performance between 40% and 80%, demonstrating the ability of the newly designed controller for a STATCOM to alleviate voltage sags, flickers, and harmonics.

In the context of carbon neutrality, the penetration ratio of renewable energy, flexible load, energy storage, and interactive equipment have been increasing, and the boundary between traditional energy producers and consumers has been getting more blurred. A new type of energy system, namely the transactive energy system (TES), has emerged. The TES uses the value (price) as a guide for market participants in optimizing decisions, realizing centralized/distributed coordination of large-scale energy systems, and developing these systems to improve energy efficiency, thus, reducing carbon emissions and improving the economy. However, the deep coupling between energy trading and physical energy flow complicates the planning, operation optimization, trading, and interaction of traditional energy systems. Based on the abovementioned background, this special issue, which focuses on the planning, operation, and trading mechanism of TES, has received considerable attention from the research community. The four papers selected for publication in this issue are briefly introduced below.

In the article “Towards transactive energy: An analysis of information-related practical issues”, Chen et al. classified existing transactive energy market mechanisms according to the potential market structure and communication networks. Three potential practical problems related to information were proposed: asynchronous computing, real reporting, and privacy protection. Each practical problem was analyzed in detail through investigation and related research. Distributed algorithms for constrained optimizations, such as flexible and asynchronous alternating direction method of multipliers (ADMM), can help solve the problem of asynchronous computing. Mechanism design methods based on the principal-agent framework and Myerson's Lemma can provide some insights into the issue of real reporting. Two main approaches to addressing the challenge of privacy protection are homomorphic encryption and differential privacy. Based on these findings, several potential research directions were proposed to provide some insights for future research.

In the article ‘Optimization of transactive energy systems with demand response: A cyber-physical-social system perspective’, Han et al. focused on the distribution system and analyzed the challenges of TES in optimal operation of demand response (DR) in the context of cyber-physical-social system. An optimized framework of TES, which integrates artificial systems, computational experiments, and parallel energy optimization for DR modelling, was proposed. A data-driven artificial DR system was constructed based on limited data. A complete information on the Stackelberg game model that describes the relationship between distribution network operators and an artificial DR system was established to simulate the response relationship between distribution network operators and power users under different price incentives. In parallel

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost-effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre-tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value-at-risk (CVaR) approach; and (2) by co-optimizing the conflicting profits of the utility and prosumers using non-dominated sorting particle swarm optimization (NSPSO) to obtain a risk-adjusted Pareto-optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid-connected CIGRE low-voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare-optimized and cost-efficient.

The development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized the power system operation, where transactive energy markets empower local energy exchanges. Transactive energy contributes to building a low-carbon energy system by better matching the distributed renewable sources and demand. Effective market mechanisms are a key part of transactive energy market design. Despite fruitful research on related topics, some practical challenges must be addressed. This review surveys three practical issues related to information exchange in transactive energy markets: asynchronous computing, truthful reporting, and privacy preservation. The state-of-the-art results are summarized and relevant multidisciplinary theories are introduced. Based on these findings, several potential research directions are suggested that could provide insights for future studies.

Non-intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed-NILM is proposed. In Fed-NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed-NILM. In addition, a comparison of Fed-NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed-NILM exhibits superior performance in terms of scalability and convergence. Fed-NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modelling capabilities of local data owners while protecting the privacy of power consumers.

This study investigates partial demagnetization faults arising from stator interturn faults in a surface-mounted permanent-magnet-type brushless direct current motor. Because of rotor demagnetization, the fault severity increases significantly owing to an increase in the stator phase current and temperature. The effect of such a fault is reflected in machine parameters such as the motor back-EMF and radial magnetic flux, which are used to analyse the characteristics of faults. A mathematical model of a machine under possible fault conditions is developed using the finite element method and advanced hybrid model approaches. Experimental investigations are conducted to validate the proposed methodology. Subsequently, the machine parameters used for fault diagnosis are employed to develop an online expert-based system that can detect, classify and estimate the percent increase in the values of the parameters to determine the fault severity of the machine under fault conditions. It is discovered that the proposed approach is suitable for industrial and commercial applications in electric vehicles, where the machine's state-of-health estimation is crucial for avoiding major faults that may result in its failure.

This paper presents a new control approach with an optimal third harmonic injection-based nearest level modulation (OTHI-NLM) technique for a large-scale solar photovoltaic (SPV) system. This system uses a single-source multilevel converter to feed solar power into the grid. The merits of the optimal third harmonic injection (OTHI) and nearest level modulation (NLM) are considered for switching the SPV converter. A unique analysis to have an adaptive optimal injection with varying solar irradiance is conducted and compared with the well-known carrier-based high-frequency switching technique. The variations of harmonics for OTHI-NLM, an optimal third harmonic pulse width modulation (OTHI-PWM), and a constant third harmonic injection method are analysed at different irradiance levels. This optimal injection provides benefits such as enhanced DC bus utilization (DBU), better harmonics performance at different solar irradiance levels. The detailed methodology of the OTHI algorithm, nearest level modulation (NLM) technique with an optimal quotient calculation, and an instantaneous optimal third harmonic injection (THI) in closed-loop are presented. The comparative analysis shows the effectiveness of the SPV grid-tied structure over well-known ones. The power quality of the renewable energy system is investigated in dynamic solar irradiations. Moreover, simulation results are presented and validated in a real-time test bench.

The increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi-prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. Herein, a market-based control mechanism in real-time markets is proposed to economically coordinate the TE among prosumers while ensuring secure system operation. Considering the dynamic characteristics of batteries and responsive demands, a model predictive control (MPC) method is used to handle the constraints between different time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between prosumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33-bus system demonstrated the efficiency and effectiveness of the proposed method and model.