Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment

Topic Information

Dear Colleagues,

The rapid development of novel energy technologies and equipment, including renewable energy, energy storage, green hydrogen, energy production, and energy conversion and consumption devices, provides opportunities for smart grids to achieve the objectives of economic security, reliability, flexibility, and low carbon. Moreover, technological advancements cannot only control energy flow but also supply an energy load via alternative sources. However, it is difficult to adapt traditional methods to the increasingly complex and changing energy environment and ensure that they meet the requirements of rapid response and intelligent decision making. Therefore, this topic focuses on utilizing the latest innovative techniques and energy equipment to guarantee the intelligent and effective operation, control, and planning of smart grids. The goals of this Topic are as follows:

1) investigate accurate models of energy systems and equipment and explore the impact of energy equipment on energy systems;

2) coordinate the control of multiple types of energy equipment to achieve the safe, economical, reliable, flexible, and environmental operation of smart grids;

3) develop advanced energy management strategies and intelligent planning schemes to improve energy efficiency;

4) apply advanced optimization technologies and/ or artificial intelligence methods for the intelligent and effective operation, control, and planning of smart grids;

5) and realize synergy among multiple energy sources to improve the flexibility of smart grids.

Topics of interest include but are not limited to the following:

1. The advanced modeling of energy systems and equipment;
2. Efficient energy management strategies for smart grids;
3. The intelligent control of multiple types of equipment for the safe operation of smart grids;
4. The planning of multiple types of energy production, conversion, and consumption devices;
5. Advanced and effective methods for the operation, control, and planning of smart grids;
6. Machine learning and deep learning for the intelligent operation of smart grids;
7. Control strategies for intelligent switch and protection equipment, the design of renewable energy inverters, and power electronic topologies;
8. High-voltage transmission technology and the technological innovation of HVDC transmission;
9. Strategies for the safe and stable operation of smart grids under extreme weather.

Dr. Pengfei Zhao
Prof. Dr. Sheng Chen
Dr. Yunqi Wang
Dr. Liwei Ju
Dr. Zhengmao Li
Dr. Minglei Bao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	4.8	2020	27.2 Days	CHF 1000	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Forecasting forecasting	2.3	5.8	2019	24.2 Days	CHF 1800	Submit
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit

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Published Papers (12 papers)

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All Electricity Energies Forecasting Processes Smart Cities Sustainability

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20 pages, 1250 KiB  

Open AccessArticle

Developing China’s Electricity Financial Market: Strategic Design of Financial Derivatives for Risk Management and Market Stability

by Hao Feng, Yidi Zhang, Zhou Lan, Kun Wang, Yizheng Wang, Sheng Chen and Changsen Feng

Energies 2024, 17(23), 5854; https://doi.org/10.3390/en17235854 - 22 Nov 2024

Abstract

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental [...] Read more.

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China’s electricity financial market and proposes derivative instruments tailored to China’s market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China’s electricity financial market, aligning with global strategies and addressing the unique challenges of China’s energy transition. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 2599 KiB  

Open AccessArticle

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

by Yang Liu, Qiuyu Lu, Zhenfan Yu, Yue Chen and Yinguo Yang

Energies 2024, 17(21), 5425; https://doi.org/10.3390/en17215425 - 30 Oct 2024

Viewed by 423

Abstract

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. [...] Read more.

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 4381 KiB  

Open AccessArticle

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

by Chen Fu, Li Lan, Su Chen, Mingxing Guo, Xiaojing Jiang, Xiaoran Yin and Chuanbo Xu

Energies 2024, 17(20), 5203; https://doi.org/10.3390/en17205203 - 18 Oct 2024

Viewed by 689

Abstract

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is [...] Read more.

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 2275 KiB  

Open AccessArticle

A Post-Disaster Fault Recovery Model for Distribution Networks Considering Road Damage and Dual Repair Teams

by Wei Liu, Qingshan Xu, Minglei Qin and Yongbiao Yang

Energies 2024, 17(20), 5020; https://doi.org/10.3390/en17205020 - 10 Oct 2024

Viewed by 487

Abstract

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to [...] Read more.

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to road damage or severe waterlogging, making some road sections impassable. Based on this, this paper first establishes a road network model to describe the dynamic changes in access performance and road damage. It provides the shortest time-consuming route suggestions for the traffic access of mobile class resources in the post-disaster recovery task of power distribution networks. Then, the model proposes a joint repair model with general repair crew (GRC) and senior repair crew (SRC) collaboration. Different types of faults match different functions of repair crews (RCs). Finally, the proposed scheme is simulated and analyzed in a road network and power grid extreme post-disaster recovery model, including a mobile energy storage system (MESS) and distributed power sources. The simulation finds that considering road damage and severe failures produces a significant difference in the progress and load loss of the recovery task. The model proposed in this paper is more suitable for the actual scenario requirements, and the simulation results and loss assessment obtained are more accurate and informative. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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15 pages, 1112 KiB  

Open AccessArticle

Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization

by Zhiming Gu, Tingzhe Pan, Bo Li, Xin Jin, Yaohua Liao, Junhao Feng, Shi Su and Xiaoxin Liu

Energies 2024, 17(19), 4801; https://doi.org/10.3390/en17194801 - 25 Sep 2024

Viewed by 966

Abstract

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar [...] Read more.

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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16 pages, 3090 KiB  

Open AccessArticle

Comprehensive Evaluation of a Pumped Storage Operation Effect Considering Multidimensional Benefits of a New Power System

by Yinguo Yang, Ying Yang, Qiuyu Lu, Dexu Liu, Pingping Xie, Mu Wang, Zhenfan Yu and Yang Liu

Energies 2024, 17(17), 4449; https://doi.org/10.3390/en17174449 - 5 Sep 2024

Viewed by 491

Abstract

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. [...] Read more.

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. The evaluation criteria are based on the values of indexes for pumped storage plants that have already been put into operation. Using this method, the operational effect of pumped storage plants with different installed capacities, regulation durations, and conversion efficiencies are comprehensively evaluated and analyzed. The calculation results show that the operation effect of a pumped storage plant with high regulation performance and high comprehensive conversion efficiency is better, indicating that the established index system and evaluation method can comprehensively and truly reflect the positive benefits brought by a pumped storage plant to a new power system. This study can provide a practical reference for the early planning and decision making of pumped storage in a new power system. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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21 pages, 2910 KiB  

Open AccessArticle

Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning

by Shruti Kalra, Ruby Beniwal, Vinay Singh and Narendra Singh Beniwal

Electricity 2024, 5(3), 585-605; https://doi.org/10.3390/electricity5030029 - 24 Aug 2024

Viewed by 1577

Abstract

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar [...] Read more.

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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18 pages, 3501 KiB  

Open AccessArticle

A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability

by Xiping Ma, Wenxi Zhen, Rui Xu, Xiaoyang Dong and Yaxin Li

Energies 2024, 17(16), 3910; https://doi.org/10.3390/en17163910 - 8 Aug 2024

Viewed by 897

Abstract

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power [...] Read more.

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 4840 KiB  

Open AccessArticle

Cyber Insurance for Energy Economic Risks

by Alexis Pengfei Zhao, Faith Xue Fei and Mohannad Alhazmi

Smart Cities 2024, 7(4), 2042-2064; https://doi.org/10.3390/smartcities7040081 - 27 Jul 2024

Viewed by 785

Abstract

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber [...] Read more.

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber intrusions in smart city infrastructures, this study introduces a two-stage hierarchical planning model for ICT-integrated multi-energy systems, emphasizing the economic role of cyber insurance. By adopting cyber insurance, smart city operators can mitigate the financial impact of unforeseen cyber incidents, transferring these economic risks to the insurance provider. The proposed two-stage optimization model strategically balances the economic implications of urban energy system operations with cyber insurance coverage. This approach allows city managers to make economically informed decisions about insurance procurement in the first stage and implement cost-effective defense strategies against potential cyberattacks in the second stage. Utilizing a distributionally robust approach, the study captures the emergent and uncertain nature of cyberattacks through a moment-based ambiguity set and resolves the reformulated linear problem using a dynamic cutting plane method. This work offers a distinct perspective on managing the economic risks of cyber incidents in smart cities and provides a valuable framework for decision making regarding cyber insurance procurement, ultimately aiming to enhance the financial stability of smart city energy operations. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 4387 KiB  

Open AccessArticle

A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty

by Molin An, Xueshan Han and Tianguang Lu

Energies 2024, 17(14), 3515; https://doi.org/10.3390/en17143515 - 17 Jul 2024

Viewed by 577

Abstract

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method [...] Read more.

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 1991 KiB  

Open AccessArticle

Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

by Weijie Wu, Dongwei Li, Hui Sun, Yixin Li, Yining Zhang and Mingrui Zhao

Energies 2024, 17(14), 3439; https://doi.org/10.3390/en17143439 - 12 Jul 2024

Viewed by 761

Abstract

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and [...] Read more.

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 3144 KiB  

Open AccessArticle

Coordinated Optimization of Hydrogen-Integrated Energy Hubs with Demand Response-Enabled Energy Sharing

by Tasawar Abbas, Sheng Chen, Xuan Zhang and Ziyan Wang

Processes 2024, 12(7), 1338; https://doi.org/10.3390/pr12071338 - 27 Jun 2024

Viewed by 925

Abstract

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable [...] Read more.

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable multi-energy system operations. Therefore, our work aims to enhance multi-energy flexibility by coordinating various energy hubs within a hydrogen-based integrated system. This study focuses on a cost-effective, ecologically sound, and flexible tertiary hub (producer, prosumer, and consumer) with integrated demand response programs, demonstrating a 17.30% reduction in operation costs and a 13.14% decrease in emissions. Power-to-gas technology enhances coupling efficiency among gas turbines, boilers, heat pumps, and chillers. A mixed-integer nonlinear programming model using a GAMS BARON solver will achieve the optimal results of this study. The proposed model’s simulation results show reduced energy market costs, total emissions, and daily operation expenses. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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