Power Load Demand Research Articles

Research on power distribution in multiple-input multiple-output magnetic coupling resonance wireless power transfer system

Aimed at different demands of power of multiple receiver loads in multiple-input multiple-output wireless power transfer (MIMO-WPT) system, in the paper, a method of calculating the excitation source at the transmitter by general circuit model is put forward to realize the power allocation of the receiver loads. First, the magnetic coupling resonance (MCR) MIMO-WPT system is made to be equivalent to a linear circuit model, and the general circuit model of MIMO-WPT system is established, which describes output transfer characteristics, transmission efficiency and output power of the MIMO-WPT system with parametric state matrix. Then, based on the power demand of each receiver load, the vector expression of the excitation source at the transmitter of MIMO-WPT system is derived by the general circuit model. At last, the simulation experiment of WPT system with 3 transmitting and 2 receiving coils is designed to verify the accuracy of the general circuit model estimation and the feasibility of the power distribution of the receiver load. Meanwhile, the application of the general model provides a feasible solution to solve different power demands of multiple receiving loads in the MIMO-MCRWPT system.

Reduction Strategy for Electrical Loads Based on Demand Priority in Power System

Background: Most non-developing and under developing countries strive hard to tackle the situation of power crisis and to combat the imbalance between the power generation and load demand, especially in the case of increasing of the population. In this situation, the load shedding scheme has been extremely implemented as a fast solution for unbalance conditions. Thus, load shedding is crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible; however its implementation is mostly undesirable. Objective: prioritize the loads according to their importance and apply reduction strategy in the demands while the supplied power to the important loads such as health care and security installation are kept intact without any interruption as possible. Methods: The conventional methods of load shedding lead to over or under shedding and this may lead to many problems with the network. Under the scheme, these methods disconnect the load or the entire feeder without considering their priorities and may not perform as anticipated. In this work, we propose a logarithmic reduction method to reduce the load according to the priority and day life criticality. The method for shedding the load base on Reduction Matrix and which in turn depend on the priority demands. Results: The higher priority demands are fed with a reliable power source by the real time monitoring of the network accompanied with power reducing for the lower priority demands. Conclusion: We test a real data sample provided by the Iraqi national grid control center in Baghdad. Our simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

Numerical Study on the Performance of a Cogeneration System of Solid Oxide Fuel Cell Based on Biomass Gasification

In order to solve the environmental pollution problem caused by winter heating of rural residential building in northern of China, in this paper a biomass gasification (BG)-solid oxide fuel cell (SOFC) combined heat and power (CHP) system has been establishedand numerically investigated. Taking a rural village around Xi’an which is an ancient city and located at central of northern China as the study object, according to heat and electricity output of the system and the heating and electrical load characteristics of the residential building of village, the energy saving ratio and economical efficiency of the CHP system under three different operation schemes compared with the traditional energy system have been analyzed. The results show that the operation scheme for heating designated rooms in rural buildings and meeting the average heat demand of users is the most energy-efficient and economical way. The primary energy saving rate and annual cost saving rate can reach 18.0% and 10.3%, respectively. When the user’s heat and power load demand is clear, the closer the system’s output heat and power ratio to the user’s heat and power load ratio, the more significant the system’s energy saving effect.

A New Grid-Connected Constant Frequency Three-Phase Induction Generator System under Unbalanced-Voltage Conditions

This paper presents a new constant frequency, direct grid-connected wind-based induction generator system (IGS). The proposed system includes a six-phase cage rotor with two separate three-phase balanced stator windings and a three-phase SV-PWM inverter which is used as a STATCOM. The first stator winding is connected to the STATCOM and is used to excite the machine. The main frequency of the STATCOM is considered to be constant and equal to the main grid frequency. In the second stator winding, the frequency of the induced emf is equal to the constant frequency, so the generator output frequency is independent of the load power demand and its prime mover speed. The second stator winding is directly connected to the main grid without an intermediate back-to-back converter. In order to regulate the IGS output active and reactive power components, a sliding mode control (SMC) is designed. Assuming unbalanced three-phase voltages for the main grid, a second SMC is developed to remove the machine output’s negative sequence currents. Moreover, a conventional PI controller is used to force the average exchanging active power between the machine and STATCOM to zero. This PI controller generates the reference value of the rotor angular speed. An adjustable speed pitch angle-controlled wind turbine is used as the IGS’s prime mover. The effectiveness and capability of the proposed control scheme have been supported by the simulation results.

Analysis of Power Grid Voltage Stability With High Penetration of Solar PV Systems

Grid integration of solar photovoltaic (PV) systems has been escalating in recent years, with two main motivations: reducing greenhouse gas (GHG) emission and minimizing energy cost. However, the intermittent nature of solar PV generated power can significantly affect the grid voltage stability. Therefore, intermittent solar PV power generation and uncertainties associated with load demand are required to be accounted for to gain a holistic understanding on power grid voltage stability with the high penetration of PV energy sources. This article presents a framework for power grid voltage stability analysis considering uncertainties associated with PV power generation and load demand using Monte Carlo simulation. Commonly used voltage stability indicators such as critical eigenvalue, line loss, reactive power margin have been considered in the proposed framework. The proposed methodology has been verified by analyzing the voltage stability of the IEEE 14 bus test system, with the high penetration of PV energy sources and considering uncertainties associated with load demand. The results provide a clear insight into the voltage stability of power grid with different penetration levels of PV energy sources into the power grid.

An Uninterruptable PV Array-Battery Based System Operating in Different Power Modes With Enhanced Power Quality

This article aims to develop a solar- battery energy storage (BES) based system, which ensures an uninterruptable supply to loads irrespective of availability of the grid. This system comprises of a solar photovoltaic (PV) array, a BES, the grid and local residential loads. A new control is implemented such that the active power demand of residential loads, is fed from the PV array, a BES unit and the utility grid. In this system, the power control operates in different power modes, which delivers the benefits to the end users with an integration of BES and an excess of PV array power, which is sold back to the grid. For this, an effective control logic is developed for the grid tied voltage source converter. Moreover, this system deals with the issue of an integrating power quality enhancement along with the power generation from the solar PV source. The cascaded delayed signal cancellation based phase locked loop is implemented for grid synchronization during the grid voltage distortion. The developed control is easily implemented in a real time controller (dSPACE-1202). Test results validate the performance of the implemented control in different operating conditions such as varying solar power generation, load variations and unavailability of the grid.

Proportional Power-Sharing Control of Distributed Generators in Microgrids

This research addresses distributed proportional power sharing of inverter-based Distributed Generators (DGs) in microgrids under variations in maximum power capacity of DGs. A microgrid can include renewable energy resources such as wind turbines, solar panels, fuel cells, etc. The intermittent nature of such energy resources causes variations in their maximum power capacities. Since DGs in microgrids can be regarded as Multi-Agent-Systems (MASs), a consensus algorithm is designed to have the DGs generate their output power in proportion to their maximum capacities under capacity fluctuations. A change in power capacity of a DG triggers the consensus algorithm which uses a communication map at the cyber layer to estimate the corresponding change. During the transient time of reaching a consensus, the delivered power may not match the load power demand. To eliminate this mismatch, a control law is augmented that consists of a finite-time consensus algorithm embedded within the overarching power sharing consensus algorithm. The effectiveness of the distributed controller is assessed through simulation of a microgrid consisting of a realistic model of inverter-based DGs.

A Discrete-Time Robust Adaptive PI Controller for Grid-Connected Voltage Source Converter with LCL Filter

In this work, it is presented a new direct discrete-time robust adaptive PI (Proportional Integral) Controller for grid-injected current control loop of a voltage source converter with LCL filter. The mathematical background is based on Robust Model Reference Adaptive Control theory. However, the proposed controller is straightforward, it does not need a reference model and has capability to track directly currents reference. This approach simplifies significantly controller design, resulting in a reformulation of parameters vector used for adaptation of adjustable gains. It turns the controller robust to unmodelled dynamics, while avoid the complexity inherent to the conventional high order adaptive controllers for grid-connected power systems. Besides, it is highlighted that proposed controller does not need resonant controllers for grid disturbance rejection, or require any knowledge of grid parameters, lines impedance or load power demand. Also, due to its simple structure, it is easily implemented and does not require a high processing capacity. Furthermore, the effectiveness of the control strategy in terms of reference tracking, harmonics content and robustness to the grid impedance variation is corroborated through experiments.

Two-Step 구조의 인공신경망 기반 Hybrid AC/DC Microgrid 운영 기법

This paper proposes an artificial neural network(ANN) based energy management system(EMS) for hybrid AC/DC microgrid with renewable energy source and distributed generation(DG). The operation of microgrid is divided into grid-connected mode and stand-alone mode according to whether or not the power converters are connected to the main AC grid. In grid-connected mode, the operation of energy storage system(ESS) is usually to maintain an appropriate value of state of charge(SOC). On the other hand, in stand-alone mode, the regenerative operation to the main AC grid is impossible, so additional control of ESS is required. However, in the case of small-scale distribution networks, data on power generation sources and load demands have non-linear characteristics, which makes it difficult to operate the EMS. Therefore, in this paper, ANN based EMS was applied to appropriately control the surplus and shortage of ESS power. The proposed ANN was constructed with a two-step structure to achieve high accuracy even with less repetitive learning. To implement the proposed method, a laboratory-scale interlinking converter based hybrid microgrid was constructed and its validity was verified through the experimental results.

DNN-Based Distributed Voltage Stability Online Monitoring Method for Large-Scale Power Grids

With the increase of power load demand and the complexity of power grid structure, the problem of voltage stability is becoming more serious, and it is urgent to study the countermeasures. In this paper, a DNN-based distributed voltage stability online monitoring method for large-scale power grids is proposed. Unlike the traditional load margin methods, the proposed method uses a local index load impedance modulus margin (LIMM) index to determine the optimal installation locations of PMUs, which is more efficiently. Moreover, the DNN is applied to learn the nonlinear relationship between the power system operation state and its corresponding LIMM. By this way, the corresponding LIMM value can be predicted through the state variables of nodes from the installed PMUs. This method can greatly improve the calculation speed of LIMM and assess the system voltage stability level in real-time, which help the system operator to judge the operating state and take measures in time. Finally, the proposed method is tested on the 14-bus system and then on the 118-bus system respectively, and the simulation results verify the effectiveness and correctness of the proposed method.

Reactive power optimization under interval uncertainty of renewable power generation based on a security limits method

With the integration of high penetration renewable power generation, the voltage security of power systems is threatened. The reactive power optimization including interval uncertainty (RPOIU) model is applied for developing a voltage control strategy to ensure that the state variables of a power grid reside within their safe operating limits under uncertainty of renewable power generation and load demand input data. However, the RPOIU model is not solved properly in terms of solution efficiency and optimization effect at present. This paper proposes a novel method for solving the RPOIU model, which is denoted as the security limits method (SLM). The proposed SLM first computes the security limits of the state variables of the RPOIU model using an interval power flow method, i.e., the optimizing-scenarios method, and then transforms the RPOIU model into a deterministic model whose constraints conform to the established security limits. The deterministic model is then efficiently solved using the interior point method. We verify that the voltage control strategy obtained by solving the deterministic model is able to ensure that the values of the state control variables satisfy the constraints of the original RPOIU model, but with more conservative security limits. This issue is addressed by modifying the security limits of the SLM through defining an interval ratio, and applying an iterative predictor-corrector procedure. The result obtained by the proposed SLM and modified SLM based on numerical simulations are compared with those obtained by a previously proposed method for effectively solving the RPOIU model, and analysis of the results demonstrates the advantages, effectiveness, and good applicability of the proposed SLM.

Development of a Comprehensive Driving Cycle for Construction Machinery Used for Energy Recovery System Evaluation: A Case Study of Medium Hydraulic Excavators

Energy recovery and hybrid power technology are new directions in construction machinery energy-saving research. Thus far, however, no uniform standard method exists to evaluate the efficiency of novel energy-saving concept systems in early stages of development. Efficiency assessment is valuable and credible only by relying on driving cycles that are consistent with actual data. As representative products of construction machinery, hydraulic excavators are multifunctional, object-uncertain, and heavily influenced by operator habits. It is therefore challenging to develop standard excavator driving cycles. Aiming at the energy efficiency evaluation of new energy-saving products characterized by energy recovery and power system optimization, taking medium-sized hydraulic excavator as an example, this paper proposes an evaluation method of energy-saving efficiency and a classification construction method of comprehensive driving cycle of hydraulic excavator based on actual operating data with load demand power and boom recoverable power as combined variables. First, based on the analysis of general driving cycle variables for excavators with different energy-saving schemes, a load demand power model and boom recoverable power model based on machine sensing data are established. Second, 10 excavators were selected at different locations in southern China, and 30 days of real-world working data were recorded. Third, according to the periodic characteristics of working data, a method for dividing microcycle operation structure is proposed. The microcycle sample space based on the data conversion of working data is constructed and classified via the clustering algorithm. Finally, a comprehensive excavator driving cycle based on the classification results is constructed through the Markov method. Results show that the energy-saving efficiency of the three classification driving cycles was 17.45%, 13.60%, and 11.88%, respectively; the comprehensive energy-saving efficiency was 15.76%. The deviations in maximum load demand power and maximum boom recoverable power between the constructed comprehensive cycle and the sample space were 7.81% and 8.61%; the average deviation of characteristic parameters was 4.26%. The comprehensive driving cycles can fairly reflect the general characteristics of real-world working conditions. The proposed construction method of comprehensive driving cycles based on sample space is therefore reliable and holds great promise for evaluating the energy-saving efficiency of new energy-saving concept systems.

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Contemporary distribution networks can be seen with diverse dispatchable and nondispatchable energy resources. The coordinated scheduling of these dispatchable resources, together with nondispatchable resources, can provide several technoeconomic and social benefits. Since battery energy storage systems (BESSs) and microturbine units (MT units) are capital-intensive, a thorough investigation of their coordinated scheduling under the economic criterion will be a challenging task while considering dynamic electricity prices and uncertainties of renewable power generation and load demand. This paper proposes a comprehensive methodological framework for optimal coordinated scheduling of BESSs with MT unit considering existing renewable energy resources and dynamic electricity prices to maximize the daily profit function of the utility by employing a recently explored modified African buffalo optimization algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps track of system states as a priori, thus resulting in an artificial intelligence-based solution technique for sequential optimization. Further, a novel concept of fictitious charges is also proposed to restrict the counterproductive operational management of BESSs. The proposed model and method are demonstrated on the 33-bus distribution system, and the obtained results verify the effectiveness of the proposed methodology.

Event-Driven Predictive Approach for Real-Time Volt/VAR Control With CVR in Solar PV Rich Active Distribution Network

The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with a local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. Besides, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.

Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty

The uncertainty of renewable distributed energy (photovoltaic, wind power, etc.) and load demand in the microgrid poses challenges to the economy and safety of microgrid operation. This paper proposes a robust optimization model of microgrid considering uncertainty to take into account the economy and robustness of microgrid operation. A two-stage robust optimization model is established to find a balance between the economy and robustness of microgrid operation. Through the optimization procedure, the robust adjustment parameters for microgrid operation can be obtained. The optimized can effectively balance the economy and robustness. The Benders dual algorithm is used to solve the established two-stage robust optimization model. The CPLEX solver is used to simulate the IEEE39-bus system to verify the feasibility and effectiveness of the method. The simulation results show that the robustness of the system can be achieved by solving the robust adjustment parameters, meanwhile the operating cost can be reduced as much as possible no matter in the buying electricity scenario or in the selling electricity scenario.

Optimal Reduction of electrical Loads Based on Priorities Developed Using a Genetic Algorithm

Most developing countries are now working to combat imbalances between power generation and load demand. In such situations, load shedding schemes have been implemented frequently as rapid solutions for unbalanced conditions to protect networks from collapsing and to sustain stability. The conventional methods of load shedding disconnect loads without considering their priorities. In this work, a logarithmic reduction method is thus proposed to reduce loads according to the priority and criticality. A Reduction Matrix is introduced as a tuning factor scaled to the size of the network, and the paper thus presents an optimisation tool based on Genetic Algorithms, developed in MATLAB, that can be applied to minimise the error between the amount of load to be reduced and the actual load reduced in electric power systems. The proposed algorithm was tested on a practical data system sample as provided by the Iraqi Ministry of Electricity from the control center of the Iraqi national grid in Baghdad, and was shown to offer optimal load reduction with reduce error, which is necessary to eliminate the impact of load reduction in electrical networks on critical loads when total demand cannot be supplied. The simulation results thus support the effectiveness and practicality of the applied method, paving the way for its possible application in power systems.

Combined capacity and operation optimisation of lithium-ion battery energy storage working with a combined heat and power system

Combined Heat and Power (CHP) systems are considered as a transitional solution towards zero carbon emissions in the next couple of decades. The current CHP systems are mainly controlled by thermal led strategy, that is, the electrical power generation depends on the thermal energy demand. The mismatch between the power generation and load demand leads to the deficient energy utilisation and economic loss. An innovative combined planning method is proposed in the paper to improve the economic gains of the CHP systems by integrating the lithium-ion battery storage system (LBSS). The paper focuses on the simultaneous optimisation of storage capacity design and operation strategy formulation of the LBSS subject to the variations of the load and power generation from CHP with consideration of LBSS degradation and cost, and Time-of-Use pricing structures. The new strategy is implemented and tested using the University of Warwick campus CHP system combined with the LBSS facilities. The results show that the method could improve the economic performance of CHP systems. The developed method is applicable to any CHP systems optimisation with integrated LBSS.

System Dynamic Modelling of Comprehensive Carrying Capacity for Power Grid Development Planning

In the face of the changing demand of economic and social development and the policy of power system reform, the carrying capacity of power grid development should be calculated to guide the future planning. Based on the causality of the factors in China’s Power Grid Planning, a System dynamics model is constructed to quantitative carrying capacity of power grid development dynamically. A series of indicators were also considered in the model, including the power transmission and distribution pricing policy, power load demand, power grid operation benefit and cost, etc. An example of a regional power grid in China is given to verify the validity and feasibility of the proposed model.

Design of Cloud Computing-Based Control Algorithm for Hybrid Power System in Smart Grid Applications

Hybrid renewable energy (HRE) models are those that have two or more renewable sources connected together with some conventional sources to serve the demand load. The objective of this article is to present a cloud-based HRE model in which a Legendre wavelet embedded neurofuzzy (NF) indirect adaptive (LNFIA) maximum power point tracking (MPPT) control of photovoltaic (PV) system is implemented for the extraction of maximum power and a Hermite wavelet-based NF indirect adaptive control (HNFIA) of solid oxide fuel cells (SOFCs) for obtaining a swift response in a grid-connected HRE system. The implementation of these two smart controls for PV systems and SOFC maintains the tradeoff among power generation and load demands. The proposed HRE model when connected with cloud can be implemented for large-scale applications. An extensive experimental analysis is carried out to ensure the effectiveness of the proposed model. The result analysis verified that the proposed model shows an effective performance.

A Method of Seamless Transitions Between Different Operating Modes for Three-Port DC-DC Converters

This paper presents the design of three-port converters (TPCs) for smooth transitions (i.e., fast settling time, and no obvious overshoot/undershoot) of 7 distinctive operating modes, depending on sources and loads scheduling. Two viable converter configurations have been identified and selected for further analysis and design of PV-battery systems. Conventionally, mode transition is achieved by assigning specific switching patterns through feedback signals and appropriate control algorithms. This incurs a delay in the response and unavoidable noise in the circuit. Additionally, in TPCs, three voltage sensors and three current sensors are generally required for decision making in mode selection, where errors in sensors may lead to an inaccurate response. This paper presents a new control strategy where the number of switching patterns is significantly reduced to 3 patterns instead of minimum 5 patterns for existing reported topologies. Therefore, decisions are simplified so that the transition occurs naturally based on the power availability and load demand but not deliberately as in the conventional method. In addition, instead of six sensors, three voltage sensors and only one current sensor are required to achieve all the necessary operations, namely, MPPT, battery protection, and output regulation. Moreover, these sensors do not participate in mode selection decision, which leads to seamless and fast mode transition. In addition, this work considers two bidirectional ports as compared with only one bidirectional port in most reported topologies. This configuration enables both standalone and DC grid-connected applications. Experimental results are reported to verify the proposed solution.