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Having been generated with a tremendous amount annually, paper waste (PW) represents a large proportion in municipal solid waste (MSW) and also a potential source of renewable energy production through the application of anaerobic digestion (AD). However, the recalcitrant lignocellulosic structure poses obstacles to efficient utilization in this way. Recently, anaerobic and microaerobic pretreatment have attracted attention as approaches to overcome the obstacles of biogas production. This study was set out to present a systematic comparison and assessment of anaerobic and microaerobic pretreatment of PW with different oxygen loadings by five microbial agents: composting inoculum (CI), straw-decomposing inoculum (SI), cow manure (CM), sheep manure (SM), and digestate effluent (DE). The hints of microbial community evolution during the pretreatment and AD were tracked by 16S rRNA high-throughput sequencing. The results demonstrated that PW pretreated by DE with an oxygen loading of 15 ml/gVS showed the highest cumulative methane yield (CMY) of 343.2 ml/gVS, with a BD of 79.3%. In addition to DE, SI and SM were also regarded as outstanding microbial agents for pretreatment because of the acceleration of methane production at the early stage of AD. The microbial community analysis showed that Clostridium sensu stricto 1 and Clostridium sensu stricto 10 possessed high relative abundance after anaerobic pretreatment by SI, while Bacteroides and Macellibacteroides were enriched after microaerobic pretreatment by SM, which were all contributable to the cellulose degradation. Besides, aerobic Bacillus in SI and Acinetobacter in SM and DE probably promoted lignin degradation only under microaerobic conditions. During AD, VadinBC27 , Ruminococcaceae Incertae Sedis , Clostridium sensu stricto 1 , Fastidiosipila , and Caldicoprobacter were the crucial bacteria that facilitated the biodegradation of PW. By comparing the groups with same microbial agent, it could be found that changing the oxygen loading might result in the alternation between hydrogenotrophic and acetoclastic methanogens, which possibly affected the methanogenesis stage. This study not only devised a promising tactic for making full use of PW but also provided a greater understanding of the evolution of microbial community in the pretreatment and AD processes, targeting the efficient utilization of lignocellulosic biomass in full-scale applications. [ABSTRACT FROM AUTHOR]

The distribution grid experiences node voltage fluctuations due to the growing uncertainty of large-scale renewable energy sources A practical solution is establishing a chance-constrained optimal model to deal with the uncertainties. However, using this method needs to know the accurate probability distribution of node power injections, which has limitations in application. Therefore, this paper proposes a distributionally robust chanceconstrained optimization method for power grid operation based on the ambiguity set of probability distributions. Firstly, considering voltage security constraints, this paper establishes a chance-constrained model to minimize the cost of active power regulation. Besides, based on the Wasserstein ambiguity set, a linearized method is proposed to convexify the objective function. Moreover, the conditional risk value (CVaR) is applied to convert the uncertain model into a deterministic model. The effectiveness of the proposed method is validated through optimization results obtained for the modified PG&E69-bus distribution grid. [ABSTRACT FROM AUTHOR]

Introduction: Understanding the intricate relationship between energy and wellbeing in informal urban settlements is essential for developing effective interventions that address the diverse needs of residents. This paper explores this nexus through a multi-dimensional lens, examining the complexities and dynamics involved in off-grid renewable energy interventions, focusing on solar microgrids in an informal settlement in Cape Town, South Africa. Methods: The paper is based on empirical research analysed through a theoretical framework articulating the pathways through which energy insecurity influences various dimensions of wellbeing, encompassing economic, physical, social, and environmental aspects. By applying this framework to our empirical research, the paper reveals the intricate interplay between neighbourhood factors, housing conditions, social processes, and economic insecurities, shedding light on both the challenges and opportunities associated with off-grid energy interventions. Results: The understanding of wellbeing presented in the paper is based on what we term the energy-wellbeing-informality nexus. Understanding the nexus necessitates: (a) moving past universalist and technocratic understandings of wellbeing, and towards a relational and networked basis for wellbeing analysis; (b) moving beyond conventional narratives of off-grid electrification as mere technical fixes, emphasizing the importance of recognizing informal settlements as sites of innovation and experimentation; (c) understanding the multi-sectoral nature of energy-related wellbeing impacts, extending beyond energy provision to encompass broader dimensions such as education, health, and social cohesion. Discussion: The paper not only advances theoretical understanding but also offers practical insights for policymakers and practitioners. It emphasizes the need for context-sensitive policymaking that acknowledges the complexities of informal settlements and fosters innovative approaches to energy service provision. By integrating energy interventions into broader development strategies and adopting a multi-sectoral perspective, stakeholders can work towards more equitable and resilient solutions that enhance the overall wellbeing of residents in informal urban contexts. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization. [ABSTRACT FROM AUTHOR]

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning's experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost. [ABSTRACT FROM AUTHOR]

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning. [ABSTRACT FROM AUTHOR]

As the proportion of new energy continues to increase, the safety and stability of the new power system are challenged, urgently requiring the allocation of new flexible resources. This paper proposes a two-stage robust capacity optimization model considering flexibility demand constraints. Firstly, the uncertain characteristics of new energy are described, and a model of flexible resource adjustment capacity is established. Then, uncertain parameters are introduced to construct a robust capacity optimization model considering supply-demand balance, solved by column constraint generation algorithm and KKT theorem. Finally, a power system in a certain region of China is selected as the simulation object for empirical analysis to verify the effectiveness of the constructed model. The results show that the two-stage robust configuration optimization model constructed in this paper can address the uncertainty of power system and the flexibility demand brought by new energy, and the planning results can achieve a balance between the safety and economy of the new power system. [ABSTRACT FROM AUTHOR]

In the context of energy conservation and emission reduction, the integration and consumption of large-scale wind and solar resources is an inevitable trend in future energy development. However, with the increase of wind and solar grid-connected capacity, the power system also requires more flexible resources to ensure safe operation. To enhance the economic efficiency of the complementary operation of wind, solar, hydro, and thermal sources, considering the peak regulation characteristics of different types of power sources, the study of the joint dispatch model of complementary utilization of various generation methods like wind, solar, hydro, thermal, and storage is of great significance for the economic dispatch of the power system. Existing studies mainly focus on traditional thermal power units or hydropower units, with few studies investigating the impact of pumped-storage power stations on the absorption of renewable energy. Firstly, this paper introduces the composition and function of each unit under the research framework and establishes a joint dispatch model for wind, solar, hydro, and thermal power. Secondly, the paper elaborates on the objective function within the model, mainly covering the operating costs of thermal power units, hydropower units, pumped storage, wind and solar units, the cost of discarding new energy, and the cost of load shedding. Subsequently, the paper presents the constraints of the system model, mainly the feasible boundaries for the operation of each unit within the system. Finally, The results of the calculations show that the proposed model reduces the total operating cost by 12% and the power abandonment rate by 82% compared to the conventional model. It is shown that the proposed model can not only significantly improve the economic efficiency of the system operation but also reduce the level of energy waste and load shedding, effectively enhancing the degree of energy utilization within the system. [ABSTRACT FROM AUTHOR]

This Research Topic aims to cover promising and novel research into biological strategies to enhance AD performance, surveillance of biogas plants, and promote AD commercialization. Anaerobic digestion, biological pre-treatment, biogas plant surveillance, bioaugmentation, direct interspecies electron transfer Keywords: anaerobic digestion; biological pre-treatment; bioaugmentation; direct interspecies electron transfer; biogas plant surveillance EN anaerobic digestion biological pre-treatment bioaugmentation direct interspecies electron transfer biogas plant surveillance 1 3 3 11/10/21 20211105 NES 211105 Anaerobic digestion (AD) is a well-recognized process for organic waste reduction, stabilization, and bioenergy recovery (Niu et al., [4]). [Extracted from the article]

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network's dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter. [ABSTRACT FROM AUTHOR]

The research on energy storage resource management is an important measure to cope with the present problem of uncertainty in the use of renewable energy, in order to explore the evolution of the research focus and future trend of energy storage resource management under the uncertainty of renewable energy, this paper adopts the method of bibliometric analysis, and conducts a visual analysis of the relevant 800 documents in the core collection of the Web of Science by using the CiteSpace to draw co-occurrence maps and analyze the collaborative networks of different countries/regions, institutions and authors in this field, and further conducted journal co-citation analysis, literature co-citation analysis and author co-citation analysis. The keyword co-occurrence, emergent analysis, and cluster co-occurrence analysis reveal the current research focus and trend in this field, and summarize and propose four future key focus directions: energy storage technology improvement, energy storage system integration, expansion of business models for energy storage resource management, and intelligent control of energy storage system, which provide new research paths for solving the problem of renewable energy uncertainty in the future. [ABSTRACT FROM AUTHOR]

The ultra-high voltage direct current (UHVDC) transmission composed of modular multilevel converters (MMC) is an important technology for large-scale centralized transmission of renewable energy. In UHVDC system, temporary faults in the AC power grid system are a high probability fault, and the fault control strategy affects the safety and reliability of system operation. This paper studies on the condition of AC power grid faults occurring at the receiving converter station. Firstly, study the system characteristics after the occurrence of AC faults, and use theoretical analysis to derive the trend of DC voltage changes of each converter valve. Then, an AC fault ride-through control strategy with high power transmission capability is proposed with hierarchical connection structure, the strategy controls the system to synchronously reduce DC voltage and AC active power after a fault, maximizing the retention of the system's transmission capacity during the occurrence of faults, thereby reduce power shock in the system. Finally, a simulation model of the dual ended system has built based on the PSCAD simulation platform. The simulation results show that when a single-phase ground fault and a three-phase ground fault occur in the high valve group at the receiving station, the system can retain about 83% and 50% of the transmission capacity during the fault period, respectively. Meanwhile, there is no serious overvoltage or overcurrent phenomenon in the system. The simulation results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper demonstrates an enhancement of power quality for a photovoltaic (PV) system connected to the grid with a hybrid energy storage system (HESS). The proposed system utilizes a ramp-rate control (RRC) strategy to limit severe fluctuations in the PV power output. Battery storage is integrated to store surplus energy generated by the PV system and is used for continuous power application. A high-power density device, known as a supercapacitor (SC), is employed to mitigate transient fluctuations in the battery. The proposed system facilitates smooth PV power generation, stabilizes the DC bus voltage (VDC), and eliminates source current harmonics induced by non-linear loads. The Shunt Active Power Filter (SAPF) discussed in this paper serves two primary purposes. Firstly, it acts as a reactive power buffer, smoothing out fluctuations and reducing current harmonic distortions. Secondly, it enables active power injection into the grid, utilizing a specific renewable solar PV source. The efficiency of the modeled compensation system is demonstrated by the sinusoidal shape of the current and the compensation of reactive power (RPC). The targeted system showcases the effectiveness of the current setup by exhibiting low total harmonic distortion (THD). The multifunctional features of the proposed system were implemented using the MATLAB/Simulink software, and the results were validated using an OP5700 Hardware-in-the-Loop (HIL) test bench. This integration of distributed power generation capabilities not only enhances the overall power quality but also improves the efficient utilization of renewable energy resources (RESs). [ABSTRACT FROM AUTHOR]

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the- loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning. [ABSTRACT FROM AUTHOR]

This article discusses the challenges of integrating renewable energy sources into power grids and the potential solutions offered by distributed learning, optimization, and control methods. The authors highlight the need for advanced sensors, actuators, and communication equipment to manage the increasing number of active endpoints in future power networks. The article includes several research papers that propose various strategies for voltage regulation, energy management, wireless sensor networks, device scheduling, virtual power plant trading, fault-tolerant control, underwater positioning and communication systems, power sharing in microgrids, and visual servoing systems. The authors emphasize that the views expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

Traditional horizontal-axis wind turbines (HAWTs) have limited efficiency in low-wind speed regions. In this paper, an integrated energy system (IES) incorporating vertical-axis wind turbines (VAWTs) is proposed; this IES is located in an oilfield management area, which can utilize the low-wind speed resources more efficiently and improve renewable energy consumption, and it also introduces a demand response model based on thermal inertia (TI), thus smoothing out the volatility caused by the VAWT. Typical output scenarios are obtained through stochastic optimization to deal with wind turbine and photovoltaic output uncertainties, and an optimal scheduling model is proposed to minimize the system operating cost. Finally, a simulation study was conducted in a micro-oilfield management area in Shandong Province, China, to demonstrate the performance of the proposed system. The results show that the IES using a VAWT and TI can increase the renewable energy consumption capacity by 87% over the conventional HAWT system, change the user behavior, increase the economic efficiency by 12%, and achieve the smoothing of load-side fluctuation of electric and thermal loads, peak shaving, and valley filling. This paper provides a feasible solution for an IES in low-wind speed areas. [ABSTRACT FROM AUTHOR]

This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic mapping incorporates the standard WOA and modifies its internal convergence factor to a nonlinear convergence mode, resulting in an improved IWOA. It is used for the weight optimization part of BILSTM. Then, the SA is introduced to optimize the learning rate, the number of nodes in hidden layers 1 and 2, and the number of iterations of BILSTM, constructing an IWOA-SA-BILSTM prediction model. Finally, through case analysis, the prediction model proposed in this paper has the highest improvement of 76.7%, 74.5%, and 45.9% in terms of Mean Absolute Error, Root Mean Square Error, and R2, respectively, compared to other optimal benchmark models, proving the effectiveness of the model. [ABSTRACT FROM AUTHOR]

The worsening of climate conditions is closely related to the large amount of carbon dioxide produced by human use of fossil fuels. Under the guidance of the goal of "carbon peaking and carbon neutrality goals", with the deepening of the structural reform of the energy supply side, the hybrid energy system coupled with renewable energy has become an important means to solve the energy problem. This paper focuses on the comprehensive evaluation of hybrid energy systems. A complete decision support system is constructed in this study. The system primarily consists of four components: 1) Twelve evaluation criteria from economic, environmental, technological, and socio-political perspectives; 2) A decision information collecting and processing method in uncertain environment combining triangular fuzzy numbers and hesitation fuzzy language term sets; 3) A comprehensive weighting method based on Lagrange optimization theory; 4) Solution ranking based on the fuzzy VIKOR method that considers the risk preferences of decision-makers. Through a case study, it was found that the four most important criteria are investment cost, comprehensive energy efficiency, dynamic payback period and energy supply reliability with weights of 7.21%, 7.17%, 7.17%, and 7.15% respectively. A1 is the scheme with the best comprehensive benefit. The selection of solutions may vary depending on the decision-maker's risk preference. Through the aforementioned research, the decision framework enables the evaluation of the overall performance of the system and provides decision-making references for decision-makers in selecting solutions. [ABSTRACT FROM AUTHOR]

Large-scale distributed renewable energy connected to the rural distribution network has given birth to a new rural distribution system with a high proportion of new energy typical characteristics, and the optimal scheduling of the new rural distribution system has become an important issue to ensure the safe and stable operation of the power grid. This paper proposes a method of active-reactive power collaborative optimization scheduling for rural power distribution system with a high proportion of renewable energy. Firstly, the active support capability evaluation model is established, and the active power support capability and reactive power support capability of rural power distribution system are quantitatively evaluated, which provides data basis and boundary conditions for the scheduling part. Then, considering power-loss cost, distribution network operation cost, and penalty cost, a method of active-reactive power collaborative optimization scheduling for rural power distribution systems with a high proportion of renewable energy is proposed. Finally, the active support capability evaluation and regulation platform of the rural power distribution system is built to provide technical support services for the safe and stable operation of the rural power distribution system. Given the problems of overload and overvoltage faced by rural power distribution systems with a high proportion of renewable energy, this paper aims to solve the key technical challenges of optimization and regulation of new rural power distribution systems. The results show that the optimized control method proposed in this paper has better security and economy, and is conducive to promoting the construction and operation of the new rural power distribution system. [ABSTRACT FROM AUTHOR]

The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs' power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations. [ABSTRACT FROM AUTHOR]

In order to better describe the uncertainty of renewable energy output, this paper proposed a novel robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets. First, an ellipsoidal uncertainty set was established using historical data on renewable energy output, and a data-driven convex hull polyhedral set was established by connecting high-dimensional ellipsoidal vertices; on this basis, an adaptive data-driven polyhedral set model was established to address the problem of high conservatism in the scaling process of convex hull polyhedral sets. Furthermore, a novel adaptive data-driven robust scheduling model for new distribution systems was established, and a column-and-constraint generation (C&CG) algorithm was used to solve the robust scheduling model. Finally, the improved IEEE-33 bus system simulation verification shows that the robust scheduling model for new distribution systems based on adaptive data-driven polyhedral sets can reduce conservatism and improve the robustness of optimization results. [ABSTRACT FROM AUTHOR]

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid's security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections. [ABSTRACT FROM AUTHOR]

Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations. [ABSTRACT FROM AUTHOR]

The increasing incorporation of new energy sources into power grids introduces significant variability, complicating traditional load frequency control (LFC) methods. This variability can cause frequent load disturbances and severe frequency fluctuations in island city microgrids, leading to increased generation costs. To tackle these challenges, this paper introduces a novel Data knowledge-driven load frequency control (DKD-LFC) method, aimed at optimizing the balance between generation cost and frequency stability in isolated microgrids with high renewable energy integration. The DKD-LFC replaces conventional controllers with agent-based systems, utilizing reinforcement learning for adaptive frequency control in complex environments. A new policy generation algorithm, based on generative adversarial-proximal policy optimization (DAC-PPO), is proposed. This algorithm extends the traditional Actor-Critic framework of the Proximal Policy Optimization (PPO) by incorporating a Discriminator network. This network evaluates whether the input state-action pairs align with current or expert policies, guiding policy updates toward expert policies during training. Such an approach enhances the algorithm's generalization capability, crucial for effective LFC application in diverse operational contexts. The efficacy of the DKD-LFC method is validated using the isolated island city microgrid LFC model of the China Southern Grid (CSG), demonstrating its potential in managing the complexities of modern power grids. [ABSTRACT FROM AUTHOR]

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind-solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration. [ABSTRACT FROM AUTHOR]

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases. [ABSTRACT FROM AUTHOR]

This paper constructs a comprehensive electricity market model in the context of China, highlighting the deviation caused by neglecting start-up costs from an engineering perspective. The model allows for the abandonment of excess wind and solar power generation, contributing to the achievement of research objectives in scenarios with a high proportion of renewable energy. Our method innovatively integrates fuel and carbon prices, clean energy expansion, and power system marginal prices according to the carbon trading rules of the Chinese power industry, providing a more accurate representation of market dynamics. Findings reveal that neglecting start-up costs can lead to significant biases in electricity prices. We demonstrate that the marginal price sometimes deviates fromthe fluctuation of the real value. While fuel and CO2 prices can be transmitted downstream, the value of new energy must be transmitted through its impact on the marginal unit. This insight is crucial for understanding the "missing money" problem in electricity markets. Based on these findings, we propose policy recommendations. We suggest considering fixed and average costs as pricing benchmarks and utilizing capacity utilization as a signal for demand response to adjust power pricing. Furthermore, we recommend trading different energy types separately in the spot market with different pricing benchmarks to ensure the homogeneity of marginal units. [ABSTRACT FROM AUTHOR]

The day-ahead dispatching of power system depends on the predicted renewable energy output and load power, which brings great challenges to the real-time power balance. This paper focuses on voltage sensitive loads (VSLs) that are widely distributed in space, thus exploring the real-time dispatching potential for VSLs. Firstly, based on the P-V relationship of VSLs clusters, a ZIP model is adopted to establish VSLs' power controllable model. Meanwhile, considering the potential voltage instability risk during the process of voltage regulation, a linear relationship between voltage safety margin (VSM) index and node power variation was derived. Then, a two-stage linear dispatching model for controllable VSLs clusters was established to meet the needs of online application. In detail, the first stage mainly focuses on evaluating the scheduling potential of VSLs clusters and the second stage determines the optimal real-time response strategy for VSLs clusters. Finally, the effectiveness and feasibility of the proposed method are verified through an improved IEEE-30 node system. [ABSTRACT FROM AUTHOR]

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source- load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model. [ABSTRACT FROM AUTHOR]

With the rapid development of photovoltaic and wind power, the penetration rate of renewable energy in power system is gradually increasing. This escalation poses a challenge as it results in a continuous reduction in the inertia and damping of the power grid, accentuating the issue of frequency stability in power systems. It is one of the effective measures to deal with this risk that renewable energy sources such as wind turbines actively provide frequency support for power grid. This paper carries out research into the examination of wind turbines' capacity to contribute to system frequency support, considering two aspects: inertia support and primary frequency regulation capabilities. Subsequently, the frequency control method of the wind turbine support system is analyzed, emphasizing the roles of rotor kinetic energy control and power reserve control in facilitating frequency support. The evaluation of the transient frequency support capability of wind turbines is introduced, incorporating factors such as control methods, controller parameters, and the duration of transient frequency support. Key metrics, including accumulated energy and frequency change rate indices during the transient frequency support stage, are proposed to quantitatively assess the transient frequency support capability of wind turbines. These indices provide a comprehensive framework for the quantitative evaluation of wind turbine transient frequency support capabilities. [ABSTRACT FROM AUTHOR]

In recent years, the installed capacity of renewable energy systems has seen rapid growth, particularly in photovoltaic (PV) power. Photovoltaic modules, being the fundamental elements of the PV system, play a crucial role in determining system performance. However, the challenge arises from the inconsistent decay rates of PV modules, which significantly impact the accuracy of PV system modeling. To address this issue, this paper introduces a novel MIC-LSTM based parameter extraction method for the single-diode PV model. This method focuses on accurately deriving PV module model parameters under various decay rates. By establishing a mapping relationship between the current-voltage (I-V) curve characteristics and the five unknown parameters in the photovoltaic module model, the proposed method demonstrates high precision in parameter extraction. Simulation and experimental verifications are carried out to validate the proposed method, where the extraction accuracy is 99.3%, 98.39%, 98.85%, 97.91%, and 98.36% for the five unknown model parameters. [ABSTRACT FROM AUTHOR]

This paper addresses the critical need to determine the stable operating limit of modular multilevel converter-based high voltage direct current (MMC-HVDC) systems, particularly concerning the integration of extensive renewable energy sources. To achieve this, the steady-state mathematical model and state-space model of bundled hydropower and photovoltaic integration through MMCHVDC systems are established. A novel methodology considering steady-state and small-signal stability constraints is proposed to compute the stable operating region of the system. The quantitative assessment reveals that diminishing AC system short-circuit capacities amplify restrictions from small-signal stability constraints, thereby reducing the system's stable operating region. Eigenvalue and participation factor analyses shed light on the pivotal factors affecting smallsignal stability in weak AC systems. To expand the system's stable operating region, a supplementary frequency damping control strategy is proposed. The theoretical analysis and calculation results are validated by building a simulation model for the bundled hydropower and photovoltaic integration through MMCHVDC systems in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Renewable energy generation (REG) has continued to grow strongly and resulted in the lower profitability of conventional generation. Meanwhile, the indeterminacy of REG will also lead to electricity balancing challenges and more volatile and less-predictable physical flows in the power grid. In this case, a capacity market is essential to motivate new investments and, therefore, ensure supply adequacy. Considering the low-carbon transition goal and the requirements of the reliability of the power system, this paper develops a reliability-constrained capacity market framework in which the reliability criterion rather than the capacity supply-demand equilibrium is taken into account. The ramping constraints, devices' random failures, and REG uncertainties are comprehensively considered in the capacity requirement determination and allocation. First, a comprehensive capacity market mechanism coordinated with multi-objective regulations is proposed to compensate the capacity providers and encourage the renewable energy transition. Then, a novel capacity market model is proposed to clear the market with reliability constraints. Moreover, to reduce the computational burden caused by the explicit consideration of reliability constraints, several techniques are applied including the root-event-based state screening technique and the adaptive Kriging metamodel. A modified IEEE-RTS-79 case is studied to illustrate the benefits of the proposed reliability-constrained capacity market model. [ABSTRACT FROM AUTHOR]

With the large-scale growth and grid connection of intermittent renewable energy such as wind and solar, the problem of increasing renewable energy curtailment rate and system backup flexibility has become increasingly prominent. In order to solve the problem of high proportion of renewable energy scientific consumption and flexible and stable operation of energy system. We propose a flexible and economical dispatch method based on data-driven multi-regional power system. For the problem of economic dispatch of multi-area power system, a mathematical calculation model is established to satisfy the constraints of unit output, system power balance, unit ramp rate, and valve point effect, and to consider the requirement of minimizing the cost of multi-area power load comprehensively. Based on data-driven, this paper adopts an improved fruit fly optimization algorithm to quickly find the global optimal solution. The calculations are performed by IEEE6 simulation test system, and the results verify the feasibility of the proposed algorithm. The improved fruit fly optimization algorithm is compared and analyzed with other algorithms considering the quality of the obtained solutions. The results show the effectiveness and superiority of the proposed algorithm in solving multi-area economic dispatching problems in real power systems. [ABSTRACT FROM AUTHOR]

The application of SOP instead of traditional tie line switches in distribution networks can im-prove the controllability, flexibility, and renewable energy accommodation of the distribution grids. Compared with traditional tie line switches, SOP have high costs and large land occupation. When power transfer is not considered, the location and capacity of SOP can be optimized through planning algorithms to reduce the overall costs. However, the main function of tie line switches is power transfer. If SOP as alternative devices do not meet this requirement, their ex-tensive application will be restricted. Therefore, this paper designs the direct current volt-age-power smooth switching control strategy, phase angle pre-synchronization control strategy, and voltage support control strategy based on the parallel structure of SOP and tie line switches to ensure stable operation during the power transfer process, through this complete control strategy, smooth switching can be achieved to keep the DC voltage stable during the switching process, effectively suppress the impulse current and maximize the utilization of SOP capacity Simulations on the SIMULINK plat-form verify the feasibility of the control strategies. [ABSTRACT FROM AUTHOR]

This document is a list of academic research papers related to optimal power flow (OPF) in power systems. The papers cover various topics, including the use of different optimization algorithms, the integration of renewable energy sources, and the application of flexible AC transmission system (FACTS) devices. The papers come from a diverse range of authors and institutions, including researchers from Pakistan, China, Saudi Arabia, and other countries. The document provides a valuable resource for library patrons conducting research on OPF and related topics in power systems. [Extracted from the article]

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas-Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided. [ABSTRACT FROM AUTHOR]

The future electric loads in the Kingdom of Saudi Arabia (KSA) are increasing significantly, particularly in the Eastern Province of the KSA. These high-rise loads are primarily driven by the operational needs of the Saudi Arabian oil company Aramco, including oil refineries, and the infrastructures of the Saudi Basic Industries Corporation (SABIC) factories. This study aims to construct a nuclear power plant in that area to supplement and support the baseload currently covered by conventional generation units powered by fossil fuels within the Saudi Electricity Company (SEC) operations. The objective function is to minimize the operational costs of the power systems to the greatest extent possible. This paper describes a case study conducted using the IBM CPLEX Optimizer software to compare the operational costs of KSA's power systems for a 24-h period. Two scenarios were considered and addressed: the first scenario without the inclusion of a nuclear power plant (NPP) and the second scenario with the inclusion of the NPP. The unit commitment problem was modeled for both scenarios. The obtained results revealed that the second scenario, which involved the penetration of the NPP, offered an optimal economic solution for operating KSA's power systems. By employing the CPLEX Optimizer software and analyzing the unit commitment problem, this study provides valuable insights into the economic advantages of integrating the NPP into the power systems of the Kingdom of Saudi Arabia. The NPP shows viability in terms of minimizing the operational costs to 32.10 $/MWh compared to the first scenario where the operational costs were 42.10 $/MWh and resulted in almost 24% reduction in operational costs. In addition, the NPP is deemed as an optimal technology to contribute to the net zero goal by 2060, where it can reduce the reliance on fossil fuel power plants and contribute to the reduction of CO2 emissions. [ABSTRACT FROM AUTHOR]

The current pursuit of ambitious decarbonization targets is driving a swift transformation of the power grid, marked by a surge in the production of renewable energy. The expansion on application of renewable energy hinges significantly on Distributed Energy Resources but system operators are grappling with challenges due to the opaque nature of DER operations. This opacity introduces considerable risks to grid stability, as the burgeoning volume of DERs may surpass the existing power network's capacity. In response, the advent of Digital Twins (DT) technology offers a viable remedy by creating virtual counterparts of the physical grid infrastructure that necessitate transmitting minimal data. Digital Twins technology circumvents the hindrances associated with real-time data flows and bolsters the transparency of the system. To foster widespread implementation of DT within the sector, it is imperative to cultivate and validate its application through practical trials. To this end, Power Hardware-in-the-Loop (PHIL) experiments are employed to juxtapose the efficacy of actual power components against that of the DT models. The experiments involve connecting Grid-forming Inverter to a Realtime Digital Simulator (RTDS) for PHIL and DT testing, allowing for an in-depth analysis of the behaviour of photovoltaic inverter. This paper elucidates a platform engineered for immediate simulation tailored to DT and PHIL approaches. This platform is designed to prototype, exhibit, and evaluate grid-forming inverters under different scenarios that are critical for power restoration. With the help of simulation exchange, Perez Model is recommended to add in the DT model to increase the accuracy comparing with the PHIL model. The entire restoration process can therefore be comprehensively represented and analysed. [ABSTRACT FROM AUTHOR]

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems. [ABSTRACT FROM AUTHOR]

With a high proportion of distributed source-grid-load-storage resources penetrating into the distribution network, multi-flow aggregated energy dispatch is essential to enhance renewable energy consumption capacity and maintain grid stability. However, intelligent energy dispatch has stringent requirements for low latency and security, which necessitates the development of secure and rapid response methods. In this paper, we combine the edge-end collaboration with container microservices architecture and propose a container and microservice empowered edge-end collaborative secure and rapid response framework for multi-flow aggregated energy dispatch service in a distribution grid. Then, a container selection optimization problem is formulated to minimize the total microservices execution delay. To cope with the dynamic environment such as electromagnetic interference, noise, and workload variation, we propose a microservice container selection algorithm based on an enhanced ant colony with empirical SINR and delay performance awareness. The proposed algorithm benefits from the local and global integrated pheromone updating methods and the empirical performance-based dynamic pheromone and heuristic information updating mechanism. Simulation results demonstrate that the proposed algorithm outperforms the existing methods in average service execution delay and convergence speed. [ABSTRACT FROM AUTHOR]

Under the background of "dual carbon" strategy, the integration of renewable energy adds volatility to the grid. Relying solely on generation-side resources for regulation is inadequate, necessitating a flexible demand response from diverse demandside resources. This paper employs a physical connection and information exchange between the distribution network and microgrids to leverage the advantages of centralized-distributed optimization. This establishes a coordinated demand response model between the distribution network and microgrids, gradually establishing a new type of distribution network that integrates interconnected grids and microgrids. This also necessitates the analysis of the response characteristics of various load resources within microgrids and the categorization and modeling of loads based on their response speeds. Additionally, a method for evaluating the multi-time scale schedulable capacity of microgrids is proposed. Finally, a coordinated demand response model between the distribution network and microgrids based on the schedulable capacity assessment is established. This model is validated through case studies, demonstrating its effectiveness. The coordinated demand response between distribution networks and microgrids enables them to operate in a collaborative and economically safe manner. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) from organic waste has gained worldwide attention because it offers significant environmental and economic benefits. It can reduce the local waste through recycling which will conserve resources, reduce greenhouse gas emissions, and build economic resilience in the face of an uncertain future for energy production and waste disposal. The productive use of local waste through recycling conserves resources by reducing landfill space, the whole of life impacts of landfilling, and post-closure maintenance of landfills. Turning waste into a renewable energy source will assist the decarbonisation of the economy by reducing harmful emissions and pollutants. Therefore, this mini-review aims to summarise key factors and present valuable evidence for an efficient AD process. It also presents the pros and cons of different AD process to convert organic waste along with the reactor technologies. Besides, this paper highlights the challenges and the future perspective of the AD process. However, it is highlighted that for an effective and efficient AD process, appropriate temperature, pH, a strong inoculum to substrate ratio, good mixing and small particle sizes are important factors. The selection of suitable AD process and reactor is important because not all types of processes and reactors are not effective for processing organic waste. This study is of great importance for ongoing work on renewable energy generation from waste and provides important knowledge of innovative waste processing. Finally, it is recommended that the government should increase their support towards the AD technology and consider the unutilized significant potential of gaseous biofuel production. [ABSTRACT FROM AUTHOR]

In the context of energy transition and carbon neutrality strategies, distributed renewable energy is widely emerging on the distribution side. However, due to the volatility and randomness characteristics of distributed renewable energy, its full absorption poses a huge challenge to the economy and stable operation of the power grid. Flexible resources on the demand side, represented by air conditioners and electric vehicles, can participate in grid dispatching, improving the economic efficiency and reliability of system operation. To this end, this paper proposes an intraday dispatch strategy for demand-side flexible resources based on two-stage optimization. First, based on a generalized energy storage model, the adjustable flexibility of demand-side flexible resources is modeled. Second, to hinder the uncertainties related with markets and demandside flexible resources, the rolling optimization is adopted for the optimal bidding, thus enabling demand-side flexible resources to participate in the intraday market with low risks of profit losses. Third, based on the bidding decision, real-time dispatch optimization is carried out to cope with the system operation deviation caused by forecast errors. Then, in the real-time control stage, the dispatch instructions are decomposed to the control units to achieve power tracking. Finally, the proposed method is simulated and verified on a test system. The results show that by coordinating the operation of demand-side flexible resources through two-stage optimization, the uncertainties of renewable energy output, market price, etc. Can be effectively overcome, and the economics of system operation can be improved. [ABSTRACT FROM AUTHOR]

With the exacerbation of environmental issues and the ongoing energy crisis, the large-scale integration of renewable energy resources has significantly increased the demand for flexibility in distribution systems. Establishing a comprehensive flexibility evaluation index system is crucial for the development, promotion, and optimization of AC/DC distribution systems. This paper focuses on the flexibility evaluation of AC/ DC distribution systems, proposing a detailed flexibility evaluation index system and a comprehensive evaluation method based on the ANP-Entropy weighting model. The proposed flexibility evaluation index system comprises 18 key indexes categorized into four essential dimensions: overall flexibility performance, flexible resources participation, operational performance, and economic performance. The ANP- Entropy weighting model is employed to perform a thorough evaluation of the flexibility of AC/DC distribution systems. To validate the effectiveness of the proposed evaluation method, a case study is conducted. Additionally, the study explores the impacts of controllable distributed generator (CDG) capacity, energy storage system (ESS) capacity, and voltage source converter (VSC) capacity on the flexibility of AC/DC distribution systems. The results provide valuable insights into optimizing the flexibility and performance of AC/DC distribution systems. [ABSTRACT FROM AUTHOR]

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers' convenience. [ABSTRACT FROM AUTHOR]

Introduction: High Alpine regions show a great potential for solar photovoltaic electricity production in winter due to the reflective properties of snow and the larger number of sun hours compared to lower urban and peri-urban regions. In countries with a pronounced deficit in domestic electricity production such as Switzerland, utility-scale projects of Alpine-PV power plants are now flourishing. The harsh environmental conditions and the complexity of the high-Alpine topography however challenge the conventional design practices. In this context, the main question the article addresses is the representation of test-scale measurement in the performances of Alpine-PV, utility-scale power plants. Methods: This article focuses on the comparison between long term tests and the first Alpine-PV utility-scale power plant in Switzerland. Results: The results show that the main performance indicators such as the Alpine-to-Midlands final yield ratio or the winter fraction, do scale. However, absolute final yield values are consistently lower at the utility scale. Discussion: The paper discusses the possible impacts of the complex Alpine topography as constraint for the design of the larger extent of utility-scale power plants as well as the influence of scale on the snow induced losses. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of solar and wind power has rapidly increased to construct renewable energy-dominated power systems (RPSs). On this basis, the forecasting errors of renewable generation power have negative effects on the operation of the power system. However, traditional scheduling methods are overly dependent on the generation-side dispatchable resources and lack uncertainty modeling strategies, so they are inadequate to tackle this problem. In this case, it is necessary to enhance the flexibility of the RPS by both mining the load-side dispatchable resources and improving the decision-making model under uncertainty during the energy and reserve co-dispatch. In this paper, due to the great potential in facilitating the RPS regulation, the demand response (DR) model of fused magnesium load (FML) is first established to enable the deeper interaction between the load side and the whole RPS. Then, based on the principal component analysis and clustering algorithm, an improved typical scenario set generation method is proposed to obtain a much less conservative model of the spatiotemporally correlated uncertainty. On this basis, a two-stage distributionally robust optimization model of the energy and reserve co-dispatch is developed for the RPS considering the DR of FML. Finally, the proposed method is validated by numerical tests. The results show that the costs of day-ahead dispatch and re-dispatch are significantly decreased by using the improved typical scenario set and considering the DR of FML in regulation, which enhances the operation economy while maintaining the high reliability and safety of the RPS. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of decarbonizing the electricity sector in the Dominican Republic, pivotal for addressing climate change and fostering economic growth. Employing the robust-decision making methodology, we studied multiple scenarios via computational models, capturing inputs from stakeholders and evaluating each scenario across 1,000 futures to capture deep uncertainty. Four scenarios were examined: baseline, reference, natural gas, and renewable. The renewable scenario emerged as the most advantageous, proposing the replacement of coal-fired power generation with renewable sources, primarily solar and wind, coupled with batteries. A significant investment, averaging US$3.3 billion, is necessary for this shift toward renewable energy; however, these investments are overcompensated by savings in operational costs. Crucially, this transition promises substantial benefits by 2050: an estimated cumulative average net economic gain of US$2.7 billion, an 8% reduction in average generation costs in 2050, the creation of 160,000 direct jobs, and the avoidance of circa 140 million tons of C02. The findings underscore the feasibility and economic viability of transitioning to a 55% renewable energy generation by 2050. The study offers a critical roadmap for policymakers, highlighting renewable energy expansion, transmission grid strengthening, and strategic coal generation replacement, thus offering a comprehensive blueprint for the nation's energy transition. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]