Power Load Demand Research Articles

An effective design method for grid-connected solar PV power plants for power supply reliability

This paper discusses a methodology, specifically for solar power potential areas, to effectively design and develop solar photovoltaic power plants integrated with battery banks connected to the utility grid as an additional backup to maintain power stability and reliability. To prove the effectiveness of this method regarding its use for the design and development of the proposed system, Kinshasa city in Democratic Republic of the Congo with a huge (5425 MWh) energy deficit has been considered as a case study. In fact, the method employed in this study has considered weather data, site selection, hourly load power and energy demand analysis, specifications of PV technologies and other components of the system. Economic analysis has also been carried out for the proposed system viability assessment. With a competitive LCOE, SPP < 10 years, NPV˃0, SIR˃1, and ROI ˃10 %, and a yearly PV energy output greater than the city's energy deficit, the system proposed has been found to be feasible and viable. In a search for better performance, higher efficiency and better economic values, the proposed method is highly recommended and can be regarded as one of the most effective and the easiest for sizing large-scale PV systems.

Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid

Electricity distribution reliability is critical for global safety, healthcare and the economy. The smart grid is defined as the combination of sensors and modern technology into the power system, which innovates the electricity generation, distribution, surveillance, and control model. Several issues must be addressed in order for the smart grid to be useful. The smart grid’s security is both a difficult task and a critical concern. An Adaptive Deep Neural Network (ADNN) will be developed in this study to secure demand-side management in an IoT-enabled smart grid. The proposed ADNN combines Deep Neural Networks (DNN) and the Squirrel Search Algorithm (SSA). The SSA is used in the DNN to improve its performance by selecting optimal weighting factors. The proposed methodology will be used to ensure that energy is used efficiently based on its priority level. It will also be used to prevent intrusions into the smart grid. A proposed classifier will classify the intrusion or dishonest entities. To optimize energy utilization and empower security, this proposed multi-agent energy management system (MEMS) and autonomous interface system (AIS) are being developed. Performance metrics such as energy consumption, load demand power, and generated power will be used to evaluate the proposed methodology, which will be implemented in MATLAB. Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) will be used to compare the proposed methodology to existing methods.

Optimal Dispatching of Ladder-Type Carbon Trading in Integrated Energy System With Advanced Adiabatic Compressed Air Energy Storage

Advanced adiabatic compressed air energy storage (AA-CAES) is a promising form of CAES technology, which can realize multi-energy cascade storage and supply. Given the characteristics of cooling, heating, and power (CHP) load demand of regionally integrated energy system (IES) and the overall need to control the total carbon emission, this study first establishes the AA-CAES multi-energy storage model; second, based on analyzing the multi-energy characteristics of AA-CAES, a ladder-type carbon trading mechanism is introduced; furthermore, aiming for minimal system operating costs and carbon trading costs, an optimal dispatch model of an integrated energy system with AA-CAES as the energy hub for coupling multiple energy forms is established. Finally, an empirical study was conducted based on the energy use scenario of a provincial and ministerial university in Qinghai Province. This study analyzes the impact of the operation of AA-CAES and the introduction of ladder-type carbon trading on the operating costs and carbon trading costs of IES. The simulation results show that the total operating cost of the proposed model is reduced by 23.9%, and carbon emission is decreased by 14.5% compared to the conventional IES. It proves the validity of coupling AA-CAES and stepped carbon trading mechanisms to facilitate low-carbon economy in IES.

A hybrid control topology for cascaded multilevel inverter with hybrid renewable energy generation subsystem

In this paper, a hybrid control topology is proposed for cascaded multilevel inverter (CMLI) with a grid-connected hybrid system involves wind and photovoltaic generation subsystem. The proposed hybrid control technique is the joint execution of Reptile Search Algorithm (RSA) and Gradient Boosting Decision Tree (GBDT) algorithm thus it is called RSA-GBDT approach. The purpose of proposed approach is to achieve load power demand and manage power regulation. Initially, the Cascade Multilevel Inverter (CMLI) is designed to obtain optimal control signal through proposed controller. The proposed control technique is utilized in two phases of the proposed system. In the first phase, RSA is used to evaluate the optimal gain parameters considering the range of source currents is also utilized to generate the optimal offline control signal data set. According to data set, in the second phase, the GBDT works and forecasts the most optimal control signals from the online CMLI. Finally, the resulting control signal is used to control CMLI's Insulated Gate Bipolar Transistor (IGBT). The proposed RSA-GBDT technique develops the operation mode for solar and wind generation system discovers the switching states of converter. The proposed control system minimizes the variation of system parameters and external disturbances. Also, the load demand is achieved and the proposed RSA-GBDT control approach is executed on MATLAB/Simulink platform and performance is analyzed.

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles.

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques.

Probabilistic optimal siting and sizing of distributed generation and shunt capacitors considering feeder flow control units using a novel distribution power flow

A modern energy system should be reliable, economically viable, and environment-friendly. This paper addresses the above issue by proposing a probabilistic approach of optimally placing and sizing distributed generation and shunt capacitors in a distribution network. Objectives are to simultaneously reduce the cost, emission, network loss, improve the system voltage profile, and avoid line overloads. A feeder flow control generator is considered so that the distribution network draws a constant amount of active and reactive power from the substation. The operation is modeled using a Zero-PQVδ bus pair in the power flow. A novel distribution system power flow is proposed to embody the operation philosophy. Uncertainties of renewable power sources and load demand are modeled using ‘Hong's 2m point estimate method’. A fuzzy multi-objective satisfaction criterion is used to realize multiple objectives. Simulation studies on a thirty-three node distribution network reveal the efficacy of the proposed method.

Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan

Renewable energy systems such as Photovoltaic (PV) have become one of the best options for supplying electricity at the distribution network level. This is mainly because the PV system is sustainable, environmentally friendly, and is a low-cost form of energy. The intermittent and unpredictable nature of renewable energy sources which leads to a mismatch between the power generation and load demand is the challenge to having 100% renewable power networks. Therefore, an Energy Storage System (ESS) can be a significant solution to overcome these challenges and improve the reliability of the network. In Jordan, the energy sector is facing a number of challenges due to the high energy-import dependency, high energy costs, and the inadequate electrification of rural areas. In this paper, the optimal integration of PV and ESS systems is designed and developed for a distribution network in Jordan. The economic and energy performance of the network and a proposed power network under different optimization algorithms and power network operation scenarios are investigated. Metaheuristic optimization algorithms, namely: Golden Ratio Optimization Method (GROM) and Particle Swarm Optimization (PSO) algorithms, are employed to find the optimal configurations and integrated 100% PV and ESS for the microgrid.

Design and implementations of high step‐up non‐isolated DC‐DC converters for electric vehicles application

AbstractAn effective hybrid energy system that connects with a fuel cell, solar, and battery system is described as a renewable energy (RE) source powered electric vehicle. The consequences of designing multi‐input DC‐DC converters for various sources are thoroughly studied. The fuel cell (FC) is utilized as the major source of energy, with photovoltaic (PV) livings utilized to recharge the battery, enhance efficiency, and diminish fuel consumption. By selecting a suitable coupled inductor turn ratio, the proposed structure efficiently enhances the rate of output voltage. A coupled inductor's created leakage inductance energy can be reclaimed, resulting in a reduction in the recommended converter's power losses. Variability in RE power production and load demand are both taken into account for maintaining the power balance. In addition, a power management technique is developed and implemented in a control technique. High gain, continuous input current, low voltage stress on switches, and extensive input and output operations are some of the interesting features of the proposed converter. The converter's results are also experimentally verified with the use of a programmable DC source.

Wind-Driven DFIG–Battery–PV-Based System With Advance DSOSF-FLL Control

In this article, a wind-driven doubly fed induction generator (DFIG), battery, and photovoltaic (PV) array-based system with advanced discrete second-order sequence filter-frequency locked loop control for grid-side converter (GSC) is presented. The improved grid-side control provides the reactive power demand of load connected at the point of common coupling. The GSC control provides active and reactive power sharing and mitigates the power quality issues at unbalanced and nonlinear load conditions. Moreover, it gives a satisfactory performance during the dynamic wind speeds and load variation. The maximum wind power is extracted using the tip speed ratio algorithm. The necessary quantity of reactive power for DFIG is delivered by the rotor-side converter (RSC). The battery with a bidirectional converter is connected to the common dc link of the DFIG. The battery is used to maintain the dc-link voltage between RSC and GSC and it stores the power in light-load conditions. It provides the load demand during lower wind speed and solar irradiation. The maximum solar power is extracted by well-established incremental conductance (InC) algorithm, which provides optimum performance during the high dynamic variation of solar irradiation. Moreover, the InC algorithm increases the system's stability and reduces costs. It is easy to implement and handle nonlinearity. The feedforward PV component is added with the active load current component to improve the dynamic behavior of the system. Simulated and test results show the performance of the developed system in different dynamic conditions, such as load unbalancing, changes in PV insolation, and change in speed from the cut-in to cut-out speeds of the wind turbine. Moreover, obtained results show the battery behavior during different dynamic conditions.

General Nash bargaining based direct P2P energy trading among prosumers under multiple uncertainties

This paper presents a transaction scheme for the direct peer-to-peer (P2P) energy trading problem among prosumers under uncertainties. Prosumers, in the distribution network, coordinate with neighbors to share the idle energy and maximize the cooperative benefit. Most of the existing researches focus on mechanism design with the assumption of accurate forecast. Nevertheless, the P2P energy trading problem is challenging due to multiple uncertainties, caused by the market price, intermittent power output and load demand variation. Although stochastic or robust optimization approaches are introduced and investigated to manage uncertainties, there are limitations for their application, such as large scenario sizes in the stochastic programming, coordination of the conservativeness and economy in the robust optimization. To address these challenges, a hybrid stochastic/robust optimization model is adopted to manage uncertainties which makes trade-offs between computational time and conservatism. The uncertain price is modeled via various scenarios based on forecast values while a robust optimization is developed to take into account the uncertainties of renewable generation and load. Based on the above uncertainties description, the direct P2P energy trading among prosumers is formulated as a general Nash bargaining (GNB) problem which is decomposed into two subproblem: operation cost minimization problem (P1) and bargaining problem (P2). As P1 and P2 are nonconvex problem, linearization techniques including KKT conditions and logarithmic transformation are employed to transform them as mixed-integer linear programming problem. A linear distflow model is formulated to address an OPF problem considering voltage limits of an AC network in a radial structure. Furthermore, a graph theoretic method is proposed to construct the incident matrix about buses and branches in the network constraints which considerably enhances the solution efficiency. Numerical case studies demonstrate that the constructed model can effectively incentivize direct P2P energy trading among prosumers, and multiple uncertainties affect the operation costs and energy transaction decisions. Additionally, the efficiency of the graph theory based modeling approach is proved for solving the energy trading model with OPF constraints.

Compressed Air Energy Storage Capacity Configuration and Economic Evaluation Considering the Uncertainty of Wind Energy

The random nature of wind energy is an important reason for the low energy utilization rate of wind farms. The use of a compressed air energy storage system (CAES) can help reduce the random characteristics of wind power generation while also increasing the utilization rate of wind energy. However, the unreasonable capacity allocation of the CAES system results in high capital investment and a long payback period. In order to improve the economic benefits of energy storage, this paper studies the capacity configuration of compressed air energy storage systems under the condition of wind energy uncertainty. First, the typical hourly power distribution of wind power generation was obtained using historical data. Factors such as user load demand, time-of-use price of the power grid, system investment cost, power shortage cost, and power sales revenue were considered. Then, a model was built with the charging and discharging power and gas storage capacity of the CAES system as constraints, and the maximum return on investment and the minimum volume of the gas storage tank as targets. NSGA-II and TOPSIS optimal selection methods were used to solve the problem. Finally, the model was used to optimize a power operation case. The results show that in the case of an hourly load power demand of a factory using 3.2 MW, a wind farm would need to keep four wind turbines running every day, and a compressed air energy storage system with a rated power of 1 MW and a rated capacity of 7 MW would ensure the best project benefit. In this mode, 1.24 × 103 MWh of wind abandoning power could be reduced annually, 2.6 × 104 kg of carbon emissions could be reduced by increasing energy storage within the operation cycle, and the payback period of investment would only be 4.8 years.

Research on coordinated control strategy of isolated DC microgrid with PV/hybrid energy storage

During the operation of DC microgrid, energy storage system plays an important role in supplying the power difference between distributed generation unit and load and maintaining the voltage stability of DC bus, in recent years, hybrid energy storage technology has gradually attracted the attention of researchers. In this paper, a power distribution control strategy of hybrid energy storage system (HESS) is studied. The droop control based on virtual capacitor is used for the converter of supercapacitor (SC) to realize the power distribution in HESS, and the control strategy is improved to solve the problem that the deviation of bus voltage caused by droop control. Based on the control strategy of HESS, a coordinated control strategy of isolated DC microgrid is studied. By considering SOC of battery and the power demand of load, 3 operation modes of DC microgrid are set. DC microgrid can work in a designated mode under various conditions to achieve efficient and stable operation. Finally, the theory of the above control strategies is verified by the simulation work in RTDS and hardware-in-the-loop experiment based on DSP28335 and RTDS.

Efficiency Improvement of Dual-Receiver WPT Systems Based on Partial Power Processing Control

In wireless power transfer (WPT) system for light electric vehicles (LEVs), adopting dual receivers on the receiver side is an available way to offer a high-current power supply. In this article, the partial power processing (PPP) control method is proposed for optimizing the power loss of the dual-receiver WPT system. With the PPP control method, only one of the active rectifiers will be regulated by the variable angle phase-shift (VAPS) modulation to adjust the required power under different load power demands and misalignment situations. As a result, the switching loss of the two active rectifiers, which accounts for a considerable part of the overall power loss with a high operating frequency, can be dramatically reduced. At last, 24 V-50 A-1200 W LEV wireless charging experimental setup to verify the effectiveness of the proposed PPP control method. Compared with the conventional power distribution method based on impedance matching with VAPS, the proposed PPP control method can increase the efficiency by 6%–7% with misalignment situations, achieving the overall efficiency as high as >92% at the heavy load.

Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation.

Management Optimization of Electricity System with Sustainability Enhancement

Based on new policies and social changes, renewable energies have highly penetrated electrical systems, making the system more vulnerable than before. On the other hand, it leads to congestion and competition within the network. To this end, this paper developed a probabilistic multi-objective-based congestion management approach and applied it to the optimal transmission switching (OTS) strategies, to maximize system suitability and minimize total production costs. A point estimation economic method (PEM) has been applied, as one of the best management and economic tools to handle the uncertainties associated with a wind turbine’s power production and load demand (LD). Results demonstrate the effectiveness and merit of the proposed technique, compared to the existing one, which can lead to higher reliability and sustainability for the grids.

The effectiveness of decentralised electro-thermal load shifting strategies in low voltage network violation management

The IPCC 1.5 °C report highlights the need to reduce UK carbon emissions by 80% 2050, and so it is essential to examine the routes that could be taken to achieve this goal. Replacement of traditional boiler systems with heat pumps (using electricity from clean sources) in residences may aid decarbonization of the energy sector. However, typical LV distribution networks are not designed to carry the increased loads will result from this change, and so it is necessary to explore strategies to mitigate any adverse effects of such loads.In this study, we examine the effect of air source heat pumps on voltage and thermal violations experienced by a typical UK LV network. The effect of decentralized heating system operational strategies (pre-heating, thermostat setback, and space heat buffering) on results is then investigated. Finally, the sensitivity of results to external temperature, network topology, and building fabric standard are examined.It is found that no mixture of strategies is sufficient to significantly improve localized voltage conditions at remote/branch points on LV feeders, as a result of the much greater sensitivity of branch node voltages to power demand of localized loads. Even where EN 50160 voltage standards are applied, elimination of violations is rarely possible.

Two-level model predictive control energy management strategy for hybrid power ships with hybrid energy storage system

Compared with the load power characteristics of ground power systems, the intermittent and random fluctuation of ship load power demand brings challenges to the energy management system of ship power systems. To achieve fuel economy and solve the problem of power fluctuation, the hybrid energy storage system (HESS) composed of the battery pack and ultra-capacitor is applied to the diesel-electric hybrid ship. A two-level model predictive control (MPC) strategy consisting of two optimization stages is proposed. The first stage aims to determine the power distribution solution of the diesel generator and battery at a large time-scale. In the second stage, the operation strategy of the ultracapacitor is determined at a small time-scale based on the power distribution solution of the first stage. The conventional MPC-based strategy and global optimization strategy based on dynamic programming (DP) are compared with the proposed strategy. The simulation results show that the proposed strategy has better fuel saving ability, and realizes the clear power frequency division between the supercapacitor and battery. In addition, the proposed strategy can also approach the performance of global optimization.

Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System

The advancement of using the cryogenic energy storage (CES) system has enabled efficient utilization of abandoned wind and solar energy, and the system can be dispatched in the peak hours of regional power load demand to release energy. It can fill the demand gap, which is conducive to the peak regulation of the power system and can further promote the rapid development of new energy. This study optimizes the various types of energy complementary to the CES system using hybrid gravitational search algorithm-local search optimization (hGSA-LS). First, the mathematical model of the energy storage system (ESS) including the CES system is briefly described. Second, an economic scheduling optimization model of the IES is constructed by minimizing the operating cost of the system. Third, the hGSA-LS methods to solve the optimization problem are proposed. Simulations show that the hGSA-LS methodology is more efficient. The simulation results verify the feasibility of CES compared with traditional systems in terms of economic benefits, new energy consumption rate, primary energy saving rate, and carbon emissions under different fluctuations in energy prices. Optimization of the system operation using the proposed hGSA-LS algorithm takes 5.87 s; however, the GA, PSO, and GSA require 12.56, 10.33, and 7.95 s, respectively. Thus, the hGSA-LS algorithm shows a comparatively better performance than GA, PSO, and GSA in terms of time.

Investigating the Complementarity Characteristics of Wind and Solar Power for Load Matching Based on the Typical Load Demand in China

This study explores the potential of renewable power to meet the load demand in China. The complementarity for load matching (LM-complementarity) is defined firstly. Kendall&#x0027;s correlation is employed to quantify the LM-complementarity. Then the complementarity characteristics on the hourly and daily time scales are analyzed. Results reveal that increasing the distance between interconnected power plants has weak improvements on the LM-complementarity in most cases. The LM-complementarity between wind and solar power is superior to that between wind or solar power generated in different regions. The hourly load demand can be effectively met by the LM-complementarity between wind and solar power. The optimal LM-complementarity scenario effectively eliminates the anti-peak regulation feature of wind power and reduces the phase differences between load demand and renewable power generation during a day. However, it is hard to balance renewable power generation and load demand on the daily time scale by the LM-complementarity. Compared with the complementarity for power smoothing defined in previous studies, the instability and peak-to-valley differences of the net-load demand can be effectively reduced by LM-complementarity. Thus the needs for flexibility are effectively reduced when integrating large-scale renewable power into the power system.

Research on running control strategy of photo-hydrogen coupling energy block system based on homogenization modeling

Abstract With the in-depth development of economy, the problem of energy transformation is imminent. Both solar energy and hydrogen energy play an important role in low-carbon energy, which can effectively reduce the dependence of the existing energy system on traditional fossil energy and reduce environmental pollution. In order to improve the consumption rate of photovoltaic power generation and the utilization rate of hydrogen energy, this paper analyzes the power exchange characteristics of heterogeneous energy, establishes the homogenization model of various heterogeneous energy, and puts forward an effective running control strategy. While making the output power of photovoltaic generator set meet the power demand of load, hydrogen energy can be maximally applied. Finally, a simulation example is given to verify the effectiveness of the running control strategy proposed in this paper.