Power Balance Problem Research Articles

Analysis of Artificial Intelligence Based MPPT in PV Grid Connected System

The There has been a rise in the demand for electrical power during the past 10 years. Installing a new power generator (PG) is a costly and time-consuming procedure. For this reason, solar power plants are considered a viable alternative for meeting the current energy demand. But crucial maintenance and output power balance are the key issues facing solar power facilities. In order to reduce output power balance and maintenance problems in solar plants, an appropriate approach is required. This study offers a novel method for tracking the maximum power for hybrid photovoltaic (PV) and wind energy systems (WES): single maximum power point tracking, or MPPT. In the proposed MPPT technique, a radial basis function network (RBFN) control mechanism is based on an artificial neural network (ANN).

A comprehensive optimization mathematical model for wind solar energy storage complementary distribution network based on multi-regulatory devices under the background of renewable energy integration

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Design of a Series–Parallel All-DC Power Generation System Based on a New DC Wind Turbine

Wind energy is a good alternative to fossil fuels, as the use of fossil fuels has seriously exacerbated the emission of greenhouse gases such as carbon dioxide and has greatly affected the environment. Conventional AC wind farms and AC transmission systems inevitably face problems involving reactive currents and overvoltage in the context of large-scale, large-capacity, and long-distance transmission. However, the use of all-DC wind turbines, together with DC convergence and DC transmission systems, has obvious advantages over AC transmission in terms of transmission losses and expandability. Such technology does not require bulky frequency transformers and can well solve the aforementioned problems of reactive currents and overvoltage. This paper proposes a new series–parallel structure for an all-DC wind power generation system. The series end uses a DC/DC converter based on the Cuk circuit to solve the current consistency and power balancing problems of the series wind turbine through current control, whereas the parallel end uses a large-capacity DC/DC converter based on the capacity transfer principle, to solve the problem of voltage consistency at the grid-connected end. The series part is used to increase the voltage level of the system, which can reduce the huge construction costs of offshore platforms, and the parallel part is used to increase the capacity of the system, which enables its incorporation into large-scale wind farms to achieve the replacement of fossil fuel energy.

Tight power balance multi-time scale disposal strategy for wind integrated system considering electric vehicle charging station

High wind power penetration and peak load pose significant challenges to the power system in maintaining the power supply–demand balance. Once the power supply–demand balance is disrupted, system load shedding and wind spillage become inevitable. In this study, the aforementioned problem is defined as the tight power balance (TPB) problem. The accuracy of wind power and load forecasting increases with a decrease in the forecasting timescale. Based on the aforementioned characteristics, a TPB multi-timescale disposal strategy for wind-integrated power systems considering electric vehicle (EV) charging stations is established to address the TPB problem at different timescales. First, the operation model of an electric vehicle charging station offering flexible ramping capacity is established. Second, a multi-timescale disposal strategy, which includes an intra-day 4-h plan, an intra-day 1-h plan, and a real-time 15-min plan, is presented by quantifying the flexible demand at different timescales. Finally, the proposed strategy is verified using a modified IEEE 118-bus system. The analysis results show that the proposed TPB multi-timescale disposal strategy effectively promotes the disposal level of the TPB problem.

Autonomous Input Voltage Sharing Control and Triple Phase Shift Modulation Method for ISOP-DAB Converter in DC Microgrid: A Multiagent Deep Reinforcement Learning-Based Method

This article proposes a multiagent (MA) deep reinforcement learning (DRL) based autonomous input voltage sharing (IVS) control and triple phase shift modulation method for input-series output-parallel (ISOP) dual active bridge (DAB) converters to solve the three challenges: the uncertainties of the dc microgrid, the power balance problem, and the current stress minimization of the converter. Specifically, the control and modulation problem of the ISOP-DAB converter is formed as a Markov game with several DRL agents. Subsequently, the MA twin-delayed deep deterministic policy gradient (MA-TD3) algorithm is applied to train the DRL agents in an offline manner. After the training process, the multiple agents can provide online control decisions for the ISOP-DAB converter to balance the IVS, and minimize the current stress among different submodules. Without accurate model information, the proposed method can adaptively obtain the optimal modulation variable combinations in a stochastic and uncertain environment. Simulation and experimental results verify the effectiveness of the proposed MA-TD3-based algorithm.

Wind Turbine Power Control According to EU Legislation

Due to high electricity prices and possible shortages of gas and other energy commodities, various fluctuations in electricity generation will need to be regulated. Given the increasing expansion of wind power plants in Europe and worldwide. It is necessary and essential to put power regulation into practice, as mandated by regulation 2016/631 EU. A significant power balance problem may arise on the grid, which may lead to cyclical and recurring blackouts in the future. The motivation for this paper is to raise awareness of the controllability of wind turbines and to highlight the gentle pace of research in this area for pitch angle control. Therefore, the chief idea of the paper is to develop a proposal for wind power plant power control by changing the rotor blade rotation, following previous research in this area, and determining the shortcomings and possibilities. The paper provides a numerical method for controlling the power output of a wind power plant, for which an algorithm has been proposed. This control is intended to provide a framework for the design and implementation of a wind power plant control program. Coordination between the multiple sources will fulfil a leading role in smooth power management.

An Economical Optimization Method for Active Power With Variable Droop Control Considering Frequency Regulation Costs in Integrated Energy Systems

As a high proportion of clean energy is connected to the power grid, the occupancy rate of the system synchronous machine decreases, the inertia constant of the system decreases, and the difficulty of frequency adjustment continues to increase. The imbalance of frequency is mainly caused by the imbalance of active power, so the problem of frequency can be transformed into the problem of active power balance. According to the droop control principle, the concept of the equivalent unit regulating power coefficient is proposed, and the equivalent unit regulating power is determined by determining the system parameters and frequency offset. In order to reduce the frequency regulation cost in the integrated energy system, a feasible method considering the frequency regulation cost is the proposed variable droop control active power economic optimization method. First, the integrated energy system in this study consists of carbon capture power plants, a wind turbine generator system (WTGS), a photovoltaic power generation system, and energy storage batteries. All four types of power supply leave spare capacity to participate in frequency regulation through droop control. Second, the concept of the equivalent unit regulating power coefficient (equivalent coefficient) and the mathematical model of the equivalent unit regulating power coefficient of the integrated energy system are put forward. Then, within the allowable range of frequency fluctuations, considering carbon trading and ancillary service markets and aiming at the lowest frequency regulation cost, an economical optimal distribution method is established for active power in an integrated energy system including carbon capture power plants, wind power, photovoltaic, and energy storage. Taking a city in the north as an example, the improved moth flame algorithm is used to solve the problem. The simulation results show that the proposed model can improve the frequency regulation characteristics and reduce the frequency regulation cost.

Multi-objective residential load scheduling approach for demand response in smart grid

• A novel scheduling approach is proposed for residential building DR programs. • A nondominated sorting genetic algorithm II (NSGA-II) is applied for multi-objective optimization. • Peak load reduction, occupant's comfort levels, and capital benefits are optimized. • Household appliances are promising flexible loads by rescheduling work time. In recent years, with the rapid growth of electricity demand and the development of smart grids, demand-side management had an important role. As the penetration rate of distributed renewable energy generation in the grid increases, the diurnal peak of the net load demand curve in the urban area is offset by renewable energy sources such as photovoltaics and the peak load time gradually shifts from afternoon to evening. The peak electricity load of residential buildings usually occurs in the evening, which aggravates the power balance problem. To this end, this study proposes a demand response (DR) scheduling approach for residential buildings, aimed for four types of residential building loads: interruptible and deferrable loads, noninterruptible and deferrable loads, noninterruptible and nondeferrable loads, and air conditioning loads. Nondominated sorting genetic algorithm II is used as a multi-objective optimization algorithm to search for the minimal electricity cost and minimal inconvenience index. Finally, the ASHRAE 140 standard building is used as a case and the proposed scheduling approach is evaluated under two scenarios of working and nonworking days. The proposed scheduling approach can effectively shave the peak load to off-peak load time, reduce electricity bills, and meet the occupants’ comfort.

Research on the Hydropower Generation Optimal Scheduling of Larger-Scale Cascade Hydropower Stations on Jinsha River and Lancang River

There are some characteristics, such as multi-dimensional, stochastic inflow, non-uniform service and scheduling subjects, in optimal scheduling of power generation. The problem is also subjected to hydrological and meteorological conditions, operation control, water demand, and loads of the power grid. Aiming at the electric power and energy balance problem in the high hydropower system, this paper uses the typical annual load curve to analyse the electricity power and energy balance, so as to better consider the influence of seasonal differences in hydrological conditions on the storage capacity of adjustable hydropower stations and the start-up of thermal power stations, and establish a mid-long term model of hydropower generation optimal scheduling of large-scale cascade hydropower stations. Taking the cascade hydropower stations in the Jinsha River and Lancang River as examples for simulation analysis, and using GUROBI to solve the model, the correctness and effectiveness of the proposed optimal scheduling model are verified.

Voltage Assessment of High PV Penetration in Radial Power Distribution Network

The total photovoltaic (PV) installed capacity of China plans to increase above 56 GW by 2050 to realize the goals of carbon peak and carbon neutrality. As a result, the hosting capacity of PV in a power distribution system affects the operation performance such as the quality of voltage, power balance, stability, and frequency problems. To investigate impacts of different PV penetration levels on the voltage and feeder in a typical power distribution network, this work considers uncertainties of PV output, and a Monte Carlo-based method is employed to simulate the impacts for different PV penetration levels on a power distribution network to evaluate voltage deviation from the normalized level (0.9 to 1.1 p.u.). A typical IEEE 33-bus system is studied and the connected PVG is based on 10-min resolution historical data from California in America in 2006. Results of the studied power distribution network are showed under three different cases: underloaded, overloaded, and fully-loaded that the 33-bus system injected with a higher PV penetration causes more voltage problems on the buses as well as the main feeder utilization tends to increase when the distribution system is underloaded or overloaded.

Primary Frequency Response Improvement in Interconnected Power Systems Using Electric Vehicle Virtual Power Plants

The smart grid concept enables demand-side management, including electric vehicles (EVs). Thus way, some ancillary services can be provided in order to improve the power system stability, reliability, and security. The high penetration level of renewable energy resources causes some problems to independent system operators, such as lack of primary reserve and active power balance problems. Nowadays, many countries are encouraging the use of EVs which provide a good chance to utilize them as a virtual power plant (VPP) in order to contribute to frequency event. This paper proposes a new control method to use EV as VPP for providing primary reserve in smart grids. The primary frequency reserve helps the power system operator to intercept the frequency decline and to improve the frequency response of the whole system. The proposed method calculates the electric vehicles’ primary reserve based on EVs’ information, such as the state of charge (SOC), the arriving time and the vehicle’s departure time. The effectiveness of the proposed scheme is verified by several simulation scenarios on a real-world modern power system with different generating units, such as conventional power plants, renewable energy resources, and electric vehicles.

Optimal Planning of Grid Scale PHES Through Characteristics-Based Large Scale Data Clustering and Emission Constrained Optimization

In today’s modern power system, the proportion of renewable energy generation is increasing. The inherent frequent variability of these energy sources creates a power balance and frequency stability problem within the power system. Planning energy storage technologies for the mitigation of this fluctuation requires an analysis of large datasets whose competition is difficult as it increases the computation burden due to the increased variable size of the dataset. The generation of wind energy scenarios based on two notable wind energy generation characteristics and the use of representative data for the generated scenarios is proposed for the optimal sizing of energy storage tools. The IEEE-30 bus system with a one year hourly average wind data of the Northern Ireland wind resource was considered for the sizing of a pumped hydro energy storage (PHES) system. Fifteen data sets were generated and used in the emission constrained optimal sizing process using code written in MATLAB R2017a and particle swarm optimization (PSO) was used as the searching algorithm. The result proves that data grouping based on the combined average and variation method gives a better optimal storage size.

Power Balance Method using Coupled Shunt Inductor and Multiple-Input Transformer for ISOP LLC Converter

High-capacity power-supply systems using a large input voltage typically improve efficiency and can be miniaturized by dividing the input voltage into multiple small voltages, thereby minimizing the stress on the switching element and thus materializing a fast switching function. When a large input voltage is divided into small voltages in series through a DC link capacitor, power is supplied to each converter and the power of each LLC (Inductor-Inductor-Capacitor) converter can be divided and converted. However, such LLC converters, which are configured by the division of the input voltage, have power imbalance due to the parameter variation between active and passive elements of the power board, which results in an increase in the stress and heat of a particular element. As this problem of power balance necessitates a design for securing a power margin and as the heated element increases its volume, the efficiency and reliability of the LLC converter are degenerated. Accordingly, this study attempted to solve the problem of the power imbalance of LLC converters at each level using a coupled shunt inductor and multiple-input transformers sharing magnetic coupling.

A Bi-Level Capacity Optimization of an Isolated Microgrid With Load Demand Management Considering Load and Renewable Generation Uncertainties

Aiming at capacity optimization of an isolated microgrid, this paper establishes a bi-level capacity optimization model that considers load demand management (LDM) while comprehensively considering load and renewable generation uncertainties. The uncertainties in this paper are brought by the source and load on the same timescale, as well as by the different characteristics of uncertainty presented over different timescales. For long timescales, the problem of source/load random uncertainty is solved using the stochastic network calculus theory to meet the energy balance constraints. For short timescales, we primarily aim to resolve the problem of power balance at the operation level, considering the uncertainty of source/load prediction errors and the impact of LDM. Particularly, by controlling the interruptible and shiftable loads, the LDM can optimize load characteristics, reduce operation costs, and increase system stability. The bi-level optimization model established in this paper is analyzed with regard to energy and power balance constraints, and the proposed mixed integer linear programming (MILP) model is solved by utilizing the CPLEX solver to minimize the investment cost. A typical microgrid, comprising a wind turbine (WT), a photovoltaic panel (PV), a controllable micro generator (CMG), and an energy storage system (ESS), is taken as an example to study capacity optimization problems. The simulation results verify the rationality and effectiveness of the proposed model and method.

Research on Control Strategy of Islanded Hybrid Microgrid Based on Hybrid Energy Storage

In order to solve the power fluctuation and power balance problem in the AC / DC hybrid microgrid, a control strategy is proposed to dynamically adjust the output power of the bidirectional AC/DC converter connected to the AC and DC microgrid by detecting the DC voltage and the AC voltage frequency. The established method can be applied to different line impedances of AC power and load interconnections. In the hybrid energy storage, the droop control is used to automatically adjust the output power of the battery, and the supercapacitor quickly provides the high frequency component of the load power to reduce the impact caused by the load mutation on the battery and the bus voltage. Finally, a hybrid microgrid simulation model is built by using Matlab/Simulink. The simulation results show that the distributed control strategy can control the stable operation and voltage stability of the hybrid microgrid.

Rate-Constrained Energy Services in Electricity

The integration of renewable generation poses operational and economic challenges for the grid. For the problem of power balance, the legacy paradigm of tailoring supply to follow demand may be inappropriate under deep penetration of renewable generation. In this situation, the alternative approach of controlling demand to follow supply offers compelling benefits in terms of reduced regulation costs. This paper considers rate-constrained energy services (RCESs) which are a specific paradigm for flexible demand. These services are characterized by a delivery window, the total amount of energy that must be supplied, and the maximum rate at which this energy may be delivered. We consider a forward market where RCES are traded. We explore allocation policies and market decisions of a supplier in this market. The supplier owns a generation mix that includes some uncertain renewable generation and may also purchase energy in day-ahead and real-time markets to meet customer demand. The supplier must optimally select the portfolio of RCES to sell, the amount of day-ahead energy to buy, and the policies for making real-time energy purchases and allocations to customers to maximize its expected profit. We offer solutions to the supplier’s decision and control problems to economically provide RCES. We provide numerical results illustrating our finds.

Solving Power Balance Problems in Single-Traction Tractors Using PTractor Plus 1.1, a Possible Learning Aid for Students of Agricultural Engineering

Tractors are used to perform jobs that require different types of agricultural tools to be attached to their rear, to their front, or both. These tools may need to be dragged, towed, or suspended above ground, and sometimes require a power supply; this is usually obtained via a hydraulic system or from the tractor’s power take-off system. When tractors have to work with such tools on different types of soils and on different slopes, the need arises to calculate the power the tractor engine will have to produce. In the classroom, this is normally calculated manually with the help of a calculator. This work, however, describes a computer program (written in Delphi and operating under Windows) that rapidly solves the most common types of power balance problems associated with single-traction tractors. The value of this software as a learning aid for students of agricultural engineering is discussed.

Optimal Operation of Emerging Flexible Resources Considering Sub-Hourly Flexible Ramp Product

To deal with high variability of stochastic market operations and with the aim of assuring a feasible and economic operation under high renewable energy sources penetration, in this paper two applicable solutions are proposed: 1) the incorporation into the electricity market of emerging flexible resources, including demand response, bulk energy storages, and plug-in electric vehicle parking Lots considering their own specific characteristics; and 2) a new flexible ramp market in order to cope with sudden variations and guarantee the rampability of reserve capacity provided by a generation portfolio. On this basis, an integrated stochastic day-ahead market clearing model has been developed to solve the energy, reserve, and flexible ramp scheduling considering a real-time power balance problem, reflecting the 5 min.-by-5 min. renewable energy source and demand changes. Numerical tests are conducted on a modified IEEE RTS 24-bus and the obtained results validate the applicability of proposed model.

Coordinated control of micro-grid based on distributed moving horizon control

This paper proposed the distributed moving horizon coordinated control scheme for the power balance and economic dispatch problems of micro-grid based on distributed generation. We design the power coordinated controller for each subsystem via moving horizon control by minimizing a suitable objective function. The objective function of distributed moving horizon coordinated controller is chosen based on the principle that wind power subsystem has the priority to generate electricity while photovoltaic power generation coordinates with wind power subsystem and the battery is only activated to meet the load demand when necessary. The simulation results illustrate that the proposed distributed moving horizon coordinated controller can allocate the output power of two generation subsystems reasonably under varying environment conditions, which not only can satisfy the load demand but also limit excessive fluctuations of output power to protect the power generation equipment.

Robust Designs of Beamforming and Power Splitting for Distributed Antenna Systems With Wireless Energy Harvesting

In this paper, we investigate a multiuser distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information (CSI). In this system, a distributed antenna port with multiple antennas supports a set of mobile stations who can decode information and harvest energy simultaneously via a power splitter. To design robust transmit beamforming vectors and the power splitting (PS) factors in the presence of CSI errors, we maximize the average worst-case signal-to-interference-plus- noise ratio (SINR) while achieving individual energy harvesting constraint for each mobile station. First, we develop an efficient algorithm to convert the max-min SINR problem to a set of "dual" min-max power balancing problems. Then, motivated by the penalty function method, an iterative algorithm based on semi-definite programming (SDP) is proposed to achieve a local optimal rank-one solution. Also, to reduce the computational complexity, we present another iterative scheme based on the Lagrangian method and the successive convex approximation (SCA) technique to yield a suboptimal solution. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.