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Abstract Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

With the sustained growth of renewable energy, the flexibility of the power system has emerged as a critical indicator, influenced by frequent peak regulation. However, a dilemma exists between maximizing flexibility and optimizing economy, and a multi-objective optimization can serve as means of coordinating conflicts. In this study, condensing cogeneration units equipped with a molten salt heat storage system is research object, an optimization model for day-ahead scheduling is established, with flexibility and economy as the primary optimization objectives. To account for various constraint condition such as unit ramp rate, unit output and thermoelectric coupling properties, an enhanced version of the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is proposed to efficiently solve the optimization problem of a molten salt heat storage system. Flexibility and economy are used to compare and analyze the non dominated solution set obtained from the Pareto front. At the same time, the heat storage and release characteristics of a molten salt heat storage system are analyzed. The findings reveal that a focus on flexibility necessitates great motten salt heat storage to manage insufficient heating during low-load operations. Conversely, an emphasis on economy leads to frequent adjustments in heat storage to maintain optimal unit performance.

With the construction and development of the new energy system, the integrated energy system (IES) has garnered significant attention as a crucial energy carrier in recent years. Therefore, to address the scheduling challenges of IES influenced by uncertainty in source load and mitigate the conservatism of scheduling schemes while enhancing clustering accuracy, a method for day-ahead top-note scheduling of IES is proposed. First, by improving dynamic time warping (DTW) for hierarchical clustering of wind, solar, and load data in IES, typical scenarios of IES are derived. Second, using the interval method to model wind, solar, and load data in IES along with their coupled devices and considering the conservatism issue of interval optimization, the established IES interval model undergoes affine processing. Finally, with the goal of minimizing the operating costs of IES, a day-ahead interval affine scheduling model is established, which is solved using the CPLEX Solver and INTLAB toolbox, and scheduling schemes for all typical scenarios are provided. Through comparative analysis of calculation examples, it is found that the method proposed in this paper can enhance clustering accuracy and reduce the conservatism of system scheduling schemes. [ABSTRACT FROM AUTHOR]

Increasing the installed capacity of renewable energy sources (RESs) in the power system is significant for advancing sustainable development. As the proportion of RESs rapidly increases in power systems, the inherent stochasticity and variability of renewable energies significantly reduce the regulatory capacity of generation resources. To compensate for the lack of power system flexibility, it is necessary to coordinate the participation of load-side resources in demand response (DR). Therefore, this paper proposes a solution to the diminished flexibility of power systems. It introduces a day-ahead stochastic scheduling model for an integrated thermal-hydro-wind-solar system. This model relies on customer directrix load (CDL) to efficiently absorb RES output. CDL represents an ideal load profile shape. Firstly, the stochastic scenario sets of RES output were modeled using Monte Carlo simulations, and the complementary characteristics between wind and solar output are considered using Copula theory. Then, CDL is introduced into day-ahead scheduling model, which considers relevant demand-side responsive load constraints. Secondly, customer-side DR effectiveness model is proposed to obtain the shaping load profile after DR, based on quantitative customer response effectiveness evaluation metrics. Lastly, system-side stochastic scheduling model of high-proportion RES power system is proposed based on the shaping load profile. Case studies were conducted on a modified IEEE-6 bus system. These studies show that the model effectively addresses the uncertainty of RES. It improves the power system's regulation capability. Additionally, it promotes the absorption of RES. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i. e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.

Energy management and optimal scheduling of microgrid play importent roles in the construction of new power system. It is of great significance to study how to improve the consumption level of renewable energy, and reduce the uncertainty risk of source and load, and optimize the system operation cost. In view of this paper, a day-ahead optimal scheduling model of independent DC microgrid with generalized energy storage based on information gap decision theory (IGDT) is proposed. Firstly, a hybrid energy storage system with super-capacitor is constructed to reduce the operating cost of batteries. The flexible load with the characteristics of 'virtual energy storage' is combined with the hybrid energy storage to form generalized energy storage, giving full play to the characteristics of flexible resources in the microgrid system. Secondly, the uncertainty of the system is considered, and the IGDT model is introduced to establish the robust model under the risk aversion strategy and the opportunity model under the risk speculation strategy based on the deterministic model, so as to pursue the maximization of risk reduction and returns from two decision-making perspectives. Finally, through the simulation analysis of an example, the influence of uncertain factors on system scheduling decision is quantified on the basis of reducing the operating cost of microgrid, and the validity and reference of the model are verified.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the construction and development of the new energy system, the integrated energy system (IES) has garnered significant attention as a crucial energy carrier in recent years. Therefore, to address the scheduling challenges of IES influenced by uncertainty in source load and mitigate the conservatism of scheduling schemes while enhancing clustering accuracy, a method for day-ahead top-note scheduling of IES is proposed. First, by improving dynamic time warping (DTW) for hierarchical clustering of wind, solar, and load data in IES, typical scenarios of IES are derived. Second, using the interval method to model wind, solar, and load data in IES along with their coupled devices and considering the conservatism issue of interval optimization, the established IES interval model undergoes affine processing. Finally, with the goal of minimizing the operating costs of IES, a day-ahead interval affine scheduling model is established, which is solved using the CPLEX Solver and INTLAB toolbox, and scheduling schemes for all typical scenarios are provided. Through comparative analysis of calculation examples, it is found that the method proposed in this paper can enhance clustering accuracy and reduce the conservatism of system scheduling schemes.

Abstract A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to electricity usage for public lighting. This paper focuses on the day‐ahead scheduling of a hybrid renewable energy system (HRES) exploiting solar–wind energy potential to meet the electrical energy needs of public lighting. The studied HRES provides electricity for a Wi‐Fi hotspot and a charging hotspot for the end users and has an energy storage system that ensures a reliable electricity supply without interruptions. The day‐ahead scheduling of the studied HRES is based on electricity generation forecasting by using a deep‐learning approach. Particularly, the long short‐term memory model is utilized, considering the fact that it is able to perceive long‐term dependencies among the time series. Moreover, the model's performance is investigated through the determination of diverse inputs: (a) historical data, (b) weather predictions, and (c) historical data with weather predictions. Multiple scenarios of energy consumption are assumed and applied to optimize the day‐ahead scheduling. A new recommendation method is proposed and applied for day‐ahead scheduling, utilizing the power forecasts to achieve optimum operation and energy savings. The results point out that the utilization of the proposed recommendation method controls loads when a shortage in power generation and battery capacity is forecasted for the day ahead, leading to significant energy savings and minimizing the power demand from the grid.

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order cone programming approach in the complex domain. The main goal of efficiently operating PV-STATCOMs in distribution networks is to dynamically compensate for the active and reactive power generated by renewable energy resources such as photovoltaic plants. This is achieved by controlling power electronic converters, usually voltage source converters, to manage reactive power with lagging or leading power factors. Numerical simulations were conducted to analyze the effects of different power factors on the IEEE 33- and 69-bus systems. The simulations considered operations with a unity power factor (active power injection only), a zero power factor (reactive power injection only), and a variable power factor (active and reactive power injections). The results demonstrated the benefits of dynamic, active and reactive power compensation in reducing grid power losses, voltage profile deviations, and energy purchasing costs at the substation terminals. These simulations were conducted using the CVX tool and the Gurobi solver in the MATLAB programming environment. [ABSTRACT FROM AUTHOR]

Considering the possible overvoltage caused by high-penetration photovoltaics (PVs) connected to the distribution networks (DNs), a cluster partition-based voltage control combined day-ahead scheduling and real-time control for distribution networks is proposed. Firstly, a community detection algorithm utilizing a coupling quality function is introduced to divide the PVs into clusters. Based on the cluster partition, day-ahead scheduling (DAS) is proposed with the objective of minimizing the operating costs of PVs, as well as the on-load tap changer (OLTC). In the real-time control, a second-order cone programming (SOCP) model-based real-time voltage control (RTVC) strategy is drawn up in each cluster to regulate the PV inverters, and this strategy can correct the day-ahead scheduling by modifications. The proposed strategy realizes the combination of day-ahead scheduling and real-time voltage control, and the optimization of voltage control can be greatly simplified. Finally, the proposed method is applied to a practical 10 kV feeder to verify its effectiveness. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and clean energy play a crucial role in addressing the pressing challenges of climate change, environmental degradation, and energy security. However, the increasing adoption of EVs brings with it several challenges related to the charging load. This paper proposes a new stochastic approach for coordinated charging/discharging of EVs in a parking lot powered with photovoltaic (PV) energy systems. The main aim of the strategy is to schedule the charging/discharging of EVs to satisfy their energy needs while minimizing the operating cost of the parking lot. In this study, the coordinated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operating modes are compared against the first-come/first-serve (FCFS) charging mode. To obtain practical results, different uncertainties associated with EVs and PV generation are considered using stochastic optimization. The uncertainties are considered using the sample average approximation method by applying the Monte Carlo Simulation. Hence, a mixed integer linear programming problem is formulated and is solved using CPLEX to minimize the expected day-ahead operating cost of the parking lot. Several Scenario studies are simulated to validate the effectiveness of the proposed approach, which achieved up to 24% reduction in operating costs on weekdays.

Compared with independent scheduling, the coupled scheduling of integrated energy system can effectively reduce the operating cost and improve the consumption of renewable energy, which has become a major research hotspot. However, in an integrated system, uncertainties from one area could directly affect another, which could reduce the reliability and result in instability of the whole integrated energy system. This paper constructs a power–gas–heating integrated system based on energy routers and energy routing algorithm to study the day-ahead coordinated optimal scheduling strategy. At the same time, the power and natural gas flow are comprehensively considered, and the structure of the multi-port energy router with integrated energy and the energy routing algorithm are proposed to realize the power optimization and energy routing between the power, gas and heating networks. The numerical case studies illustrate that the energy router and proposed cooperative scheduling strategy can improve the energy supply reliability, scheduling flexibility, renewable energy consumption and economic benefits of the integrated energy system.

With the deepening of the coupling degree between power network and natural gas network, the traditional independent optimal scheduling of transmission and distribution integrated electricity–gas systems cannot make full use of the flexible control ability of various resources and the complementary support capabilities between different energy systems. Therefore, this paper puts forward a cooperative optimization scheduling model of transmission–distribution​ integrated electricity–gas systems (TD-IEGS). The coordinated operation of multi-energy coupling devices including gas-fired units, combined heat and power (CHP) units and electric boilers (EB) are considered in this paper to enhance system operation flexibility and economy. The second order cone relaxation (SOCR) method is utilized to tackle the non-linear power and gas flow constraints, and the proposed mixed-integer non-linear programming co-optimization scheduling model is transformed into a mixed integer second order cone programming model (MISOCP). Case studies demonstrate that compared with the independent operation mode, the proposed co-optimization scheduling model of TD-IEGS can reduce total operation cost by about 6.7% under 50% wind power penetration level. Moreover, the co-optimization of TD-IEGS can promote wind power utilization especially in a high share of wind energy.

A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to electricity usage for public lighting. This paper focuses on the day‐ahead scheduling of a hybrid renewable energy system (HRES) exploiting solar–wind energy potential to meet the electrical energy needs of public lighting. The studied HRES provides electricity for a Wi‐Fi hotspot and a charging hotspot for the end users and has an energy storage system that ensures a reliable electricity supply without interruptions. The day‐ahead scheduling of the studied HRES is based on electricity generation forecasting by using a deep‐learning approach. Particularly, the long short‐term memory model is utilized, considering the fact that it is able to perceive long‐term dependencies among the time series. Moreover, the model's performance is investigated through the determination of diverse inputs: (a) historical data, (b) weather predictions, and (c) historical data with weather predictions. Multiple scenarios of energy consumption are assumed and applied to optimize the day‐ahead scheduling. A new recommendation method is proposed and applied for day‐ahead scheduling, utilizing the power forecasts to achieve optimum operation and energy savings. The results point out that the utilization of the proposed recommendation method controls loads when a shortage in power generation and battery capacity is forecasted for the day ahead, leading to significant energy savings and minimizing the power demand from the grid. [ABSTRACT FROM AUTHOR]

Most of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaining user comfort in terms of the operating waiting time of appliances. The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming, which involves constraints and consumer preferences. For optimal scheduling, the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm. Two techniques are proposed to manage the trade-off between the conflicting objectives. The first technique is the Pareto-optimal solutions classification using supervised learning methods. The second technique is called the lexicographic method. The simulations were performed based on residential building energy consumption, time-of-use pricing (TOU) and critical peak pricing (CPP). The algorithms were implemented in Python. The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio (PAR) by 28% and 49.32%, respectively, for the TOU tariff rate, and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer's comfort level. [ABSTRACT FROM AUTHOR]

The integration of variable Renewable Energy Sources (vRES) to alleviate greenhouse gas emissions has introduced significant challenges for power systems operations. These challenges include high levels of uncertainty due to the intermittence associated with vRES and therefore impose the need to devise a reliable and cost-effective day-ahead unit commitment and power and reserves scheduling for real-time operations. Also, this increasing penetration of vRES requires higher ramping capabilities from units originally designed for other purposes (e.g., base-load generation), which might be exacerbated during contingency states. Hence, in this work, we propose a methodology to address the day-ahead Contingency-Constrained Unit Commitment (CCUC) problem that leverages the participation of Battery Energy Storage Systems (BESSs) to address load-following and post-contingency management, therefore alleviating the ramping burden on conventional thermal generators. To do so, we formulate a three-level optimization problem that represents the decision-making process of obtaining the least-cost commitment, generation and reserves scheduling, while restricting the Conditional Value-at-Risk (CVaR) of the system imbalance at real-time operations to user-defined tolerance levels. In addition, we devise a computationally efficient solution approach for the proposed problem based on the Column-and Constraint Generation (CCG) algorithmic framework. Two numerical experiments are conducted to empirically illustrate the benefits of the proposed methodology. Key results indicate a reduction in real-time ramping needs and a better usage of the system resources, with a reduction in the overall system commitment levels and reserve scheduling costs when compared to a benchmark case in which storage is not available. • Risk-aware storage role in markets under renewable uncertainty and possible outages. • Novel power and reserves least-cost scheduling including storage systems. • Trilevel model to potentialize the use of storage to mitigate real-time imbalance. • Computationally efficient solution approach based on the CCG algorithmic framework. • Results indicate a reduction in real-time ramping needs and lower commitment costs. [ABSTRACT FROM AUTHOR]

• Two-stage stochastic resiliency-oriented scheduling considering failure in main-grid. • Day-ahead scheduling considering real-time uncertainties in renewables and disaster. • Analyzing the optimal operation under normal and worst realization in uncertainties. • Modeling contingent situation under high penetration of PV generation and disasters. The renewable distribution network holds tremendous potential for economic advantages and sustainability improvements within the power industry. However, resilient operations in post-disaster scenarios present significant challenges for the system. Operational risks arise from uncertain resources, making day-ahead scheduling complex for the distribution grid's operator. Therefore, real-time modeling is considered more suitable to effectively manage these uncertainties. The hydrogen system (HSYS) is also an emerging technology that improves the technical and operational characteristics of distribution networks, particularly in challenging and unpredictable situations. The study presents a comprehensive framework for evaluating HSYSs within a photovoltaic-focused power distribution network, focusing on day-ahead resiliency-oriented operations (RESOP) while considering risk factors associated with uncertain incidents. The effectiveness and feasibility of implementing the proposed HSYS in day-ahead risk-based resilient scheduling of the distribution network are analyzed using a specific distribution system. The model is formulated as a MILP problem, and the impacting uncertainties are modeled using stochastic programming. The primary risk associated with uncertainties is captured using the conditional value-at-risk (CVaR) approach, which involves the possible worst realizations of uncertainties in the model. The study's findings demonstrate that the integration of distributed HSYSs leads to significant cost savings of up to 16 % under normal conditions and 1.25 % when considering risk modeling. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) can aggregate massive distributed resources to participate in power system operation. Different types of resources have been investigated to construct VPPs. Internet data centers (IDCs) are regarded as flexible power consumers that can potentially cooperate with power systems. This paper exhibits the insight for constructing VPPs based on IDCs while comprehensively considering the operational flexibility of the workloads and power supply devices. On the workloads, this paper models the temporal and spatial flexibility of workloads in detail. On the power supply devices, this paper explores their spare capacity with respect to the power supply reliability requirement. Then, a day-ahead scheduling method of the IDC-based VPPs is established based on the operational flexibility modeling. Case studies validate the proposed approach. Moreover, because of the coupling of workload dispatch and power supply device operation, the synergy effect does not equal the sum of its parts. • Exploring the operational flexibility of IDCs to construct VPPs.. • Quantifying the potential of backup power under the reliability requirement. • Realizing the coordination of the backup power and computation workloads in IDCS. • Proposing a day-ahead scheduling framework for IDC-based VPPs. [ABSTRACT FROM AUTHOR]

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order cone programming approach in the complex domain. The main goal of efficiently operating PV-STATCOMs in distribution networks is to dynamically compensate for the active and reactive power generated by renewable energy resources such as photovoltaic plants. This is achieved by controlling power electronic converters, usually voltage source converters, to manage reactive power with lagging or leading power factors. Numerical simulations were conducted to analyze the effects of different power factors on the IEEE 33- and 69-bus systems. The simulations considered operations with a unity power factor (active power injection only), a zero power factor (reactive power injection only), and a variable power factor (active and reactive power injections). The results demonstrated the benefits of dynamic, active and reactive power compensation in reducing grid power losses, voltage profile deviations, and energy purchasing costs at the substation terminals. These simulations were conducted using the CVX tool and the Gurobi solver in the MATLAB programming environment.

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the unprecedented rising urbanization level and proliferation of distributed energy sources (DES) are motivating the transition of smart building from consumer to prosumer. In the context of mitigating upstream emission via reducing reliance on utility grids, the key challenge becomes realizing autonomous and economically optimal energy management within smart buildings. This paper innovatively proposes a Decentralized Autonomous Organization (DAO)-based energy management framework for smart buildings, where the mechanism of day-ahead scheduling DAO considering peer-to-peer (P2P) energy sharing is specifically investigated. Besides, a sequential preference satisfaction mechanism is designed to customize participants' special demands, thereby achieving effective energy matching. Considering potential communication failures between buildings in practical operations, this study develops a communication failure robust distributed algorithm to minimize operational costs and safeguard building's privacy. Extensive numerical studies for highly DES penetrated smart buildings successfully offers an operation cost reduction of 33.7% when considering preference satisfaction and P2P energy sharing in the day-ahead scheduling problem. Simulation results also demonstrates that the algorithm in this work excels in resisting potential communication failure during operation. • A DAO-based energy management framework is proposed for smart buildings. • Peer-to-peer energy sharing is incorporated in the day-ahead scheduling problem. • A preference satisfaction mechanism is modeled to customize participant's demand. • A communication failure robust distributed algorithm is developed. [ABSTRACT FROM AUTHOR]

Hydropower, characterized by its quick ramping speed and energy buffering capacity, has the ability to respond to the flexibility demand caused by the forecast uncertainty of variable renewable energy (VRE). However, how to determine the flexibility supply of each hydropower station to ensure the reliable execution of the day-ahead scheduling in the intraday operation is a challenging issue for a hydro–wind–solar hybrid system (HWSHS). Due to complex characteristics of cascade hydropower stations, there is a mutual influence between the day-ahead scheduling and its execution. Therefore, flexibility response mode (FRM) is proposed to determine the flexibility supply of each hydropower station according to the flexibility demand of VRE, and a co-optimization model is constructed to optimize the day-ahead scheduling and the parameters of FRM considering the forecast uncertainty of VRE. A HWSHS in Guizhou province, China, is employed as a case study. The results suggest that the FRM can ensure the reliable execution of the day-ahead scheduling in the intraday operation. Compared to a contrast model without FRM, the execution deviation and its duration are decreased by more than 22% and 37%, respectively, in both long-run and out-of-sample analyses. • Flexibility response mode is proposed to determine the flexibility supply. • A scenario generation method is developed to describe the forecast uncertainty. • The day-ahead scheduling and parameters of flexibility response mode are co-optimized. • The methodology is reliable and robust for executing the day-ahead scheduling. [ABSTRACT FROM AUTHOR]

Considering the possible overvoltage caused by high-penetration photovoltaics (PVs) connected to the distribution networks (DNs), a cluster partition-based voltage control combined day-ahead scheduling and real-time control for distribution networks is proposed. Firstly, a community detection algorithm utilizing a coupling quality function is introduced to divide the PVs into clusters. Based on the cluster partition, day-ahead scheduling (DAS) is proposed with the objective of minimizing the operating costs of PVs, as well as the on-load tap changer (OLTC). In the real-time control, a second-order cone programming (SOCP) model-based real-time voltage control (RTVC) strategy is drawn up in each cluster to regulate the PV inverters, and this strategy can correct the day-ahead scheduling by modifications. The proposed strategy realizes the combination of day-ahead scheduling and real-time voltage control, and the optimization of voltage control can be greatly simplified. Finally, the proposed method is applied to a practical 10 kV feeder to verify its effectiveness.

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts. • Bi-level energy management strategy for optimal operation of grid tied microgrids. • Load shifting approach that reshapes the load according to the target load profile. • A novel real-time scheduling strategy of grid tied microgrids under uncertainties. [ABSTRACT FROM AUTHOR]

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities. • Optimization of power distribution network with hydrogen systems. • Analysis impacts of hydrogen system to mitigate natural disasters' impacts. • Optimal day-ahead scheduling under uncertainty in photovoltaics and disasters. • Risk evaluation using effective risk tool to reach trade-off between risk and cost. [ABSTRACT FROM AUTHOR]

A high-quality solar power forecasting system that strictly adheres to grid regulations is valuable for system operators to formulate strategies for power system scheduling. Some grid operators, therefore, enact penalty schemes for the forecasts submitted by photovoltaic (PV) plant owners, as a means to fortify truthful and high-quality forecast submissions. From the perspectives of both plant owners and grid operators, this study inquires into the quality-to-value mapping of solar forecasts in the context of power system day-ahead scheduling. A physics-based solar power forecasting method is presented, which consists of two steps. Firstly, ensemble numerical weather prediction (NWP) is summarized into point forecasts. Then irradiance is converted to power via a physical model chain. The results reveal that the two-step physics-based forecasting method has an advantage over a winning method in Global Energy Forecasting Competition 2014 in terms of several accuracy measures. Subsequently, the economics of solar forecasting is quantified through performing day-ahead scheduling on a modified IEEE 30-bus system with PV and battery storage. It is demonstrated that, by respecting the statistical theory on consistency and elicitability when extracting point forecasts from NWP ensembles, both power system operators and PV plant owners can benefit profoundly in terms of cost savings. The former sees fewer needs for reserves, while the latter is less penalized. The data and Python code used to produce the results are also provided to enhance the reproducibility of this work. [Display omitted] • Solar power forecasts are integrated into a power system day-ahead scheduling framework. • The statistical theory on consistency and elicitability is used to summarize ensemble NWP. • Achieved 5% cost savings on power system reserve needs. • Achieved up to 82% cost savings on penalty of submitted forecasts. [ABSTRACT FROM AUTHOR]

This study makes a significant contribution to advancing smart energy management systems, crucial for effective control of energy supply and demand. Traditional methods for optimal energy management in power grids often encounter computational challenges due to the nonline ar and discrete nature of the optimization problem. In response, our research introduces a novel two-step Optimal Day-Ahead Scheduling approach for an Advanced Distribution System. This system incorporates Renewable Energy Sources, Distributed Generation units, Electric Vehicles, and Energy Storage Systems, while also facilitating surplus electricity sales to electricity markets. The proposed optimal day-ahead scheduling involves both central and local controllers collaborating to optimize a unified program. The Active Distribution System engages in electricity transactions with multiple descending energy hubs, including Combined Cooling, Heating, and Power plants. Each energy hub (EH) operator optimizes day-ahead scheduling problems, submitting bids to the distribution system operator. This unique approach integrates Demand Load Curtailment and Time-of-Use schemes as demand response programs in descending energy hubs. Addressing the inherent complexity of this optimization challenge, the study introduces a novel path-finding algorithm leveraging an improved local search operator. The application of this proposed method to a 33-bus system demonstrates significant advantages. Results showcase a notable increase of up to 193% in the benefit of selling electricity, coupled with an impressive 84% reduction in distribution system operating costs, considering base values. This contribution highlights the effectiveness of the proposed approach in optimizing energy transactions and enhancing the overall efficiency of distribution system operations. [ABSTRACT FROM AUTHOR]

Summary: Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible generation‐demand model. An optimization algorithm using mixed integer linear programming (MILP) has been formulated to minimize the operating cost and emission of dispatchable power generation, with the help of demand response. Usually, in renewable energy–based grid‐connected microgrid, the batteries are managed under partial state of charge (SoC) conditions due to the limit of power imported from grid. The proposed MILP model ensures full SoC operation and safe charging or discharging dynamics of the battery in order to enhance its lifespan. Moreover, the day‐ahead scheduling of household appliances is carried out using a novel hybrid knapsack method, which combines binary and fractional knapsack algorithms. An electric vehicle battery is considered as a flexible power load, which offers an unique way of approach in scheduling of appliances. The results confirm that the power demanded by the appliances is fulfilled at the user‐specified hour for maximum comfort along with minimum operating cost of microgrid. Generic algebraic modeling system (GAMS) tool is used to run the proposed algorithms. [ABSTRACT FROM AUTHOR]

The expansion of the power network and integration of wind farms pose challenges in the short-circuit current (SCC) problem. Transmission switching performs better in both flexibility and effectiveness compared with other SCC restriction measures, while the power grid security would be threatened as the number of switched-off-lines increases. This paper proposes a day-ahead scheduling model considering commitment of units and N-1 criterion, to avoid the excessive SCC problem caused by wind farms integration. Specially, the SCC calculation model of the grid-connected wind farms is put forward to aggregate wind farms and calculate SCC. A novel SCC formulation considering transmission switching, commitment of units as well as wind farms integration is deduced and converted to SCC constraints. The SCC constrained mixed-integer linear programming (MILP) based day-ahead scheduling model is proposed, which minimizes system operation cost and transmission switching cost. In addition, the N-1 security requirement is considered in the proposed day-ahead scheduling model to ensure system security by avoiding switching off too many lines. Numerical results of a modified IEEE 30-bus system with two wind farms illustrate effectiveness of the proposed model.

Due to the lack of support from the main grid, the intermittency of renewable energy sources (RESs) and the fluctuation of load will derive uncertainties to the operation of islanded microgrids (IMGs). It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs. With the high penetration of RESs, it faces both economic and environmental challenges if we only use spinning reserve for reserve support. To solve these problems, a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation, load, and storage. The operation risk due to reserve shortage is modeled by the conditional value-at-risk (CVaR) method. The correlation of input variables is considered for the forecasting error modeling of RES and load, and Latin hypercube sampling (LHS) is adopted to generate the random scenarios of the forecasting error, so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches. Furthermore, an optimal day-ahead scheduling model of joint energy and reserve considering risk-based reserve decision is established to coordinate the security and economy of the operation of IMGs. Finally, the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.

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In order to make Thermostatically Controlled Loads (TCLs) better meet the scheduling requirements, a day-ahead scheduling of equivalent energy storage model that takes into account of the minimum-on-off time is established. By considering the minimum-on-off time, the charging and discharging power, as well as the energy storage are modified, and the relationship between heat exchange power and energy storage are developed. By this way, the equivalent energy storage model more accurately reflects the real thermodynamic characteristic of TCLs and enables TCLs to exert the actual potential to participate in the scheduling. Finally, the simulation results verify the feasibility of the proposed scheduling method.

In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.

As fossil fuels dry up and the proportion of renewable energy increases, multi-energy system (MES) becomes an effective way to realize renewable energy accommodation. High penetration of renewable energy brings challenges to the operation of power system, such as frequent curtailment of wind and the uncertainty of renewable energy threaten the security of the system. This paper proposes a two-stage robust scheduling model of MES considering integrated heat-electricity demand response (DR) to derive robust operation decisions. The proposed robust model minimizes the operation costs under base-case scenario, while guaranteeing all operation constraints are met in any scenarios. Power-to-Gas (P2G) equipment which converts surplus wind and photovoltaic generation into natural gas is included to effectively reduce curtailment of renewable generation. Furthermore, integrated heat-electricity DR, fully utilizing the couplings among various systems, is proposed to decrease operation costs and enhance system security against uncertainties. Column-and-Constraint Generation (CCG) method is used to effectively solve the proposed robust model. Numerical results in MATLAB indicate that robust optimization ensures secure operation of the system, and the integrated heat-electricity DR could promote the accommodation of renewable energy as well as enhance economic benefits and robustness of the system. [ABSTRACT FROM AUTHOR]

With the increasing share of variable and limitedly predictable renewable energy in power systems worldwide, ensuring reserve capacity to maintain the balance of supply and demand becomes more important. On the other hand, the development of the virtual power plant model (VPP) allows renewable sources and energy storage to participate in reserve service. This paper addresses the optimal reserve bidding strategy problem of a VPP comprising of renewable energy resources (RESs), energy storage systems (ESSs), and several customers. The VPP participates in balance capacity (BC), day-ahead (DA), and intra-day (ID) markets. The scheduling problem is formulated as a two-stage chance-constrained optimization model taking the uncertainty of RESs production, load consumption, and probability of reserve activation into account. The response of VPP after its reserve capacity is called and generated is also considered to increase the operational flexibility of VPP. The proposed model is implemented on a test VPP system, and the effects of RESs sizing, ESSs sizing, and the probability of reserve activation are analyzed. Results indicate that the proposed model can perform well under real-world conditions. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Single hydrogen production methods cannot meet the stable hydrogen demand for many engineering applications under carbon emission requirements. This paper proposes a hydrogen energy hub (HEH) for stable and green hydrogen production from variable renewable energy inputs. The HEH integrates water electrolysis, biomass gasification, and natural gas reforming, which are coupled and coordinated with complementary utilization of both materials and energy. The uncertainties associated with biomass moisture content (MC) and photovoltaic (PV) generation for hydrogen production are considered. The impact of MC on the production rate of biomass is quantified and embedded in a thermodynamic equilibrium model, and inaccurate PV forecasts are also taken into account. A systematic model of HEH is developed to improve the economic benefit of system operation while providing probabilistic guarantees on the robustness of stable hydrogen production. A double-loop robust algorithm is adapted to efficiently solve the robust model, with the outer loop developing a day-ahead scheduling scheme and the inner loop figuring out the worst-case scenario. Finally, the effectiveness of the proposed model is demonstrated through case studies in terms of cost reduction, decision robustness, and computational efficiency. A 9.9% improvement in energy efficiency can be achieved through complementary utilization. The robust optimization is able to achieve an average total cost reduction of 37.52% over the deterministic optimization in 1000 random scenarios. • A hydrogen energy hub integrating electrolysis, gasification, and reforming is proposed. • A 9.9% energy efficiency increase by using material and energy complementarity. • A two-stage scheduling model and robust optimization approach are proposed. • The uncertainties in PV generation and biomass MC for hydrogen production are considered. [ABSTRACT FROM AUTHOR]

With the comprehensive progression of China's electric power reform, the participation of hydropower in the spot market has become inevitable. For hydropower-dominated power grids, the tight hydraulic and electric coupling relationship between cascade hydropower plants and the policy requirement of clean energy consumption greatly increase the challenge of optimal scheduling. Based on the market characteristics and actual demand of the hydropower-dominated power grids, this paper discusses the day-ahead spot market trading mechanism to promote the guaranteed consumption of renewable energy. On this basis, a day-ahead optimal scheduling model is established which aims to minimize the comprehensive power purchase cost of the power system. The nonlinear factors in the scheduling model, such as the relationship between the water level and storage capacity of hydropower plants, the relationship between the water level and water consumption rate, and the constraints on the start-up and shutdown durations of thermal power units, are linearized through multiple linearization techniques. The original nonlinear model is transformed into a mixed integer linear programming (MILP) model and is then solved using a CPLEX solver. The developed model is verified by taking 10 thermal power plants and 22 hydropower plants in Yunnan Power Grid as a case study. The calculation time is 183.6s, the comprehensive power purchase cost of the system is 155.35·million CNY, and no abandoned water is generated in the three basins. The simulation results show that the proposed model has high solution accuracy and efficiency, and maximizes the hydropower consumption under the premise of meeting the minimum comprehensive power purchase cost. The findings of this study can provide a scientific basis for the development of the day-ahead spot market of hydropower-dominated power grids. [Display omitted] • A day-ahead spot market trading mechanism to promote consumption of renewable energy. • Day-ahead optimal scheduling of hydropower-dominated power grids under a spot market environment. • A novel MILP approach is employed to solve the model. • Proposed model has high solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The power-to-gas (P2G) technology-a promising way to connect power and gas networks- will be widely used in the power-gas-heating integrated system (PGHIS) to promote its operational flexibility. However, the higher operational costs will affect the application of P2G on PGHIS scheduling, which cannot be ignored. In this paper, a PGHIS scheduling model considering the P2G cost and wind power utilization is proposed to analyze the impact of the P2G cost characteristics. First, a PGHIS including P2G is introduced, and different kinds of P2G operational costs are clarified to capture the cost characteristics of the P2G. Then, a multi-objective day-ahead scheduling model of the PGHIS is proposed to coordinate the tradeoff between its operational costs and the wind power utilization. Finally, the ε-constraint and entropy weight methods are used to solve the multi-objective model. The numerical results demonstrate that P2G cost characteristics can significantly affect the day-ahead scheduling of the PGHIS, and the proposed model can effectively achieve a compromise solution.

The integration of large-scale stochastic renewable energy, the aging of transmission facilities, and the growth of load demand all contribute to the increasing congestion levels of transmission systems. Such factors pose considerable stress on the economical and secure operation of power systems and the accommodation of large-scale renewable energies. However, under the smart grid circumstance, some cutting-edge transmission technologies can bring potential cost-effective solutions to leverage the potential capacity of existing transmission infrastructures. Such technologies can help the utilities to deal with the rapid change of operating conditions of the power system in a more flexible manner. For example, the network topology optimization (NTO) technology can change the transmission topology based on the operating conditions, which increases the flexibility of the transmission system. Dynamic thermal rating (DTR) can evaluate the maximum transmission capacity of transmission lines dynamically according to the weather condition parameters around the conductor. These two cost-effective technologies are promising in improving the congestion mitigation performance and can contribute to the efficient utilization of transmission network-so they will bring potential economic and reliability benefits. This paper incorporates NTO and DTR in the network-constrained unit commitment (NCUC) framework to study their synergistic effect on the power system day-ahead schedule. Case studies are performed on a modified RTS-79 system. The numerical results verify that the coordination of NTO and DTR may help decrease the generation cost and wind power curtailment.