Special Issue "Intelligent Energy Management of Electrical Power Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: 30 September 2019

Special Issue Editor

Guest Editor
Prof. Dr. Manuela Sechilariu

Sorbonne University, Université de Technologie de Compiègne, AVENUES EA 7284, France
Website | E-Mail
Interests: microgrids, hierarchical control, supervisory control, energy management, power optimization, demand response, demand-side management, photovoltaic-powered systems, Petri Nets modeling

Special Issue Information

Dear Colleagues,

The smart grid implementation is facilitated by microgrids development. Microgrids are considered the main smart grid building blocks. Whether they are AC or DC, high voltage or low voltage, high power or small power, or integrated into the distribution system or the transmission network, microgrids always require an intelligent energy management that is integrated in the power system. A comprehensive intelligent energy system is aimed at providing overall energy efficiency with regard to the following: increased power generation flexibility, improved energy consumption, reduced CO2 emission, improved stability, and minimized energy cost. This Special Issue focuses on recent key theoretical and practical developments that concern the models, technologies, and flexible solutions to facilitate the following optimal energy and power flow strategies: the techno-economic model for optimal sources dispatching (mono and multi-objective energy optimization), real-time optimal scheduling, and real time optimization with model predictive control. Special interest is given to intelligent energy management design, methodology, control principles, possible industrial applications, and techno-economic and social acceptability coupling (taking into account the human, societal, and economic factors in all design processes).

Prof. Dr. Manuela Sechilariu
Guest Editor

Manuscript Submission Information

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Keywords

  • microgrids
  • transmission and distribution power network
  • energy storage systems
  • energy optimization
  • advanced control technologies
  • real-time simulation (HiL, PHiL) and experimental validation
  • demand side management (demand response and smart loads)
  • energy monitoring
  • energy efficiency indicators
  • energy cost optimization
  • electric vehicles charging stations
  • building-integrated energy management and monitoring system
  • techno-economic and social acceptability coupling and analysis

Published Papers (11 papers)

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Research

Open AccessArticle
A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
Appl. Sci. 2019, 9(9), 1776; https://doi.org/10.3390/app9091776
Received: 20 March 2019 / Revised: 4 April 2019 / Accepted: 7 April 2019 / Published: 29 April 2019
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Abstract
The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has [...] Read more.
The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
Appl. Sci. 2019, 9(9), 1748; https://doi.org/10.3390/app9091748
Received: 15 March 2019 / Revised: 12 April 2019 / Accepted: 22 April 2019 / Published: 27 April 2019
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Abstract
The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging [...] Read more.
The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
When Energy Trading Meets Blockchain in Electrical Power System: The State of the Art
Appl. Sci. 2019, 9(8), 1561; https://doi.org/10.3390/app9081561
Received: 19 February 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 15 April 2019
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Abstract
With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many [...] Read more.
With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Optimal Configuration of Integrated Energy System Based on Energy-Conversion Interface
Appl. Sci. 2019, 9(7), 1367; https://doi.org/10.3390/app9071367
Received: 26 January 2019 / Revised: 20 March 2019 / Accepted: 25 March 2019 / Published: 1 April 2019
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Abstract
Aiming at the optimal configuration of a regional integrated energy system (IES), this paper proposes an energy-conversion interface (ECI) model that simplifies the complex multienergy network into a multi-input–multioutput dual-port network, consequently achieving the energy-coupling relationship between the energy-supply side and the demand [...] Read more.
Aiming at the optimal configuration of a regional integrated energy system (IES), this paper proposes an energy-conversion interface (ECI) model that simplifies the complex multienergy network into a multi-input–multioutput dual-port network, consequently achieving the energy-coupling relationship between the energy-supply side and the demand side. An optimized configuration model of the ECI was constructed by considering economic performance, such as device-installation cost, operation and maintenance cost, and environmental cost, as well as energy-saving performance, such as energy-utilization efficiency. Then, the ECI optimal-configuration model was established by taking a campus in northern China as an example. To verify the validity of the model, device planning quantity and daily energy scheduling of the integrated energy system of the campus were obtained by solving the model with the particle-swarm optimization method. Finally, sensitivity analysis of the system to energy prices and the reweight approach for the targets are also given in this paper, providing a decision-making basis for system planning. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration
Appl. Sci. 2019, 9(5), 1022; https://doi.org/10.3390/app9051022
Received: 1 February 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 12 March 2019
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Abstract
This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the [...] Read more.
This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs
Appl. Sci. 2019, 9(2), 356; https://doi.org/10.3390/app9020356
Received: 10 December 2018 / Revised: 3 January 2019 / Accepted: 13 January 2019 / Published: 21 January 2019
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Abstract
Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach [...] Read more.
Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Integrated Energy System Optimization Based on Standardized Matrix Modeling Method
Appl. Sci. 2018, 8(12), 2372; https://doi.org/10.3390/app8122372
Received: 31 August 2018 / Revised: 15 November 2018 / Accepted: 18 November 2018 / Published: 23 November 2018
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Abstract
Aiming at the optimization of an integrated energy system, a standardized matrix modeling method and optimization method for an integrated energy system is proposed. Firstly, from the perspective of system engineering, the energy flow between energy conversion devices is used as a state [...] Read more.
Aiming at the optimization of an integrated energy system, a standardized matrix modeling method and optimization method for an integrated energy system is proposed. Firstly, from the perspective of system engineering, the energy flow between energy conversion devices is used as a state variable to deal with nonlinear problems caused by the introduction of scheduling factors, and a standardized matrix model of the integrated energy system is constructed. Secondly, based on the proposed model, the structural optimization (i.e., energy flow structure and equipment type), design optimization (i.e., equipment capacity and quantity), and operation optimization for the integrated energy system can be achieved. The simulation case studies have shown that the proposed integrated energy system standardized matrix modeling method and optimization method are both simple and efficient, and can be effectively used to decide the system components and their interconnections, and the technical characteristics and daily operating strategy of the system components. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies
Appl. Sci. 2018, 8(11), 2293; https://doi.org/10.3390/app8112293
Received: 25 September 2018 / Revised: 30 October 2018 / Accepted: 30 October 2018 / Published: 19 November 2018
Cited by 1 | PDF Full-text (5193 KB) | HTML Full-text | XML Full-text
Abstract
In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into [...] Read more.
In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into a multi-energy complementary system (MECS). Firstly, a scenario-generating technique is proposed for uncertainty factors using the Wasserstein method and the improved K-medoids theory. Then, a multi-objective model and solution algorithm are constructed under the objectives of attaining the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations. Meanwhile, the influence of different ESD operation modes on MECS operation is discussed, specifically, the longest life cycle (LLC) and the optimum economic efficiency (OEE). Thirdly, in order to solve the multi-objective model, a solution algorithm is proposed by using the rough set method to convert the multi-objective model into a single objective model based on the payoff table. Moreover, the complementary features of the MECS are evaluated in terms of the load tracking degree, HS secondary peaking capacity, and units of coal consumption. Finally, the improved IEEE 14-bus system is chosen for the simulation analysis. The results show that (1) the proposed uncertainty simulation method can efficiently generate the most representative scenarios; (2) MECSs can utilize complementary power sources, the OEE mode can better optimize MECS scheduling, and the LLC mode can ensure the ESDs’ life cycles; (3) the scheduling scheme of MECS operation reach the optimal level when the capacity ratio of ESD:WPP–PV iso [0.62, 0.77] in the OEE mode and [1, 1.08] in the LLC mode on a typical summer day, and the ratio is [0.92, 1] in the OEE mode and [1.23, 1.31] in the LLC mode on a typical winter day. Therefore, the proposed model provides effective decision-making support for designing the optimal plan for MECS operation. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessFeature PaperArticle
Power Management Strategy for an Autonomous DC Microgrid
Appl. Sci. 2018, 8(11), 2202; https://doi.org/10.3390/app8112202
Received: 11 October 2018 / Revised: 31 October 2018 / Accepted: 6 November 2018 / Published: 9 November 2018
Cited by 1 | PDF Full-text (5393 KB) | HTML Full-text | XML Full-text
Abstract
Owing to the intermittent nature of renewable energy, microgrids in islanding operation mode require backup power sources. The diesel generator is the most popular backup source, but does not offer an instantaneous start-up and cannot immediately provide the necessary power. Therefore, supercapacitors are [...] Read more.
Owing to the intermittent nature of renewable energy, microgrids in islanding operation mode require backup power sources. The diesel generator is the most popular backup source, but does not offer an instantaneous start-up and cannot immediately provide the necessary power. Therefore, supercapacitors are used to assist the power balance during diesel generator start-up thanks to their responsiveness and high-power density. This paper proposed a power management strategy for an autonomous DC microgrid based on a photovoltaic source, electrochemical storage, a supercapacitor, and a diesel generator. The proposed control system aimed at power balance while accounting for the slow start-up characteristic of the diesel generator, the self-discharge of the supercapacitor, the dynamic load management, and the economic operating mode of the diesel generator. The main contribution of this paper centered on a power management strategy solving the above issues integrally, and economic analysis for the diesel generator and microgrid. Experimental studies were carried out for different scenarios and the results obtained confirmed the effectiveness of the proposed strategy. Furthermore, the study provided a comparison between the economic operating and load-following modes of the diesel generator and demonstrated that the economic operating mode of the diesel generator can reduce the total energy cost of the DC microgrid. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
The State of the Art of the Control Strategies for Single-Phase Electric Springs
Appl. Sci. 2018, 8(11), 2019; https://doi.org/10.3390/app8112019
Received: 25 September 2018 / Revised: 16 October 2018 / Accepted: 20 October 2018 / Published: 23 October 2018
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Abstract
The concept of electric springs (ESs) has been proposed as a new solution for stabilizing power grid fed by intermittent renewable energy sources. With a battery or active power source (DC, on the inside), the ESs can provide both active and reactive power [...] Read more.
The concept of electric springs (ESs) has been proposed as a new solution for stabilizing power grid fed by intermittent renewable energy sources. With a battery or active power source (DC, on the inside), the ESs can provide both active and reactive power compensations. So far, three typical topologies of single-phase ESs have been reported. Unlike traditional devices where power generation follows the load demand, the ESs are associated with non-critical loads form the so-called smart loads that transfer the fluctuated power to the non-critical loads, adaptively, according to the intermittent nature of power generation. After reviewing the main control strategies of single-phase ESs, the paper analyzes their advantages and disadvantages as well as their suitable applications. Comparisons among different control strategies on a specific topology version are implemented. Finally, conclusions and possible future trends are pointed out. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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Open AccessArticle
Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms
Appl. Sci. 2018, 8(9), 1654; https://doi.org/10.3390/app8091654
Received: 22 August 2018 / Revised: 9 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
Cited by 1 | PDF Full-text (3451 KB) | HTML Full-text | XML Full-text
Abstract
In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to extract important features to reveal households’ characteristics based on electricity usage data. The main goal of the analysis is to automatically extract, in a non-intrusive [...] Read more.
In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to extract important features to reveal households’ characteristics based on electricity usage data. The main goal of the analysis is to automatically extract, in a non-intrusive way, number of socio-economic household properties including family type, age of inhabitants, employment type, house type, and number of bedrooms. The knowledge of specific properties enables energy utilities to develop targeted energy conservation tariffs and to assure balanced operation management. In particular, classification of the households based on the electricity usage delivers value added information to allow accurate demand planning with the goal to enhance the overall efficiency of the network. The approach was evaluated by analyzing smart meter data collected from 4182 households in Ireland over a period of 1.5 years. The analysis outcome shows that revealing characteristics from smart meter data is feasible, and the proposed machine learning methods were yielding for an accuracy of approx. 90% and Area Under Receiver Operating Curve (AUC) of 0.82. Full article
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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