Load Priority Research Articles

A Peak Demand Control Algorithm for Multiple Controllable Loads in Industrial Processes

The peak demand or demand limit control is an important part of the actions that industries carry out to optimize their energy consumption and reduce the costs related to their electricity billing. Prioritized switching of multiple appliances is often needed in order to reduce demand and energy consumption during peak load periods. The present article describes a peak load limitation algorithm that estimates the optimal disconnection time for one or more electrical loads before the electric demand exceeds a preset limit. This algorithm uses parametric and variable load factors that vary dynamically depending on what loads are present at a given time. For its validation, a software-in-the-loop testbed was designed and developed, in which multiple electrical loads were simulated via LabVIEW software and connected to a PLC controller emulated through CODESYS software. In this environment, several test configurations were executed and evaluated to study the influence of variables such as the nominal power and the disconnection priority of loads in the algorithm output. The results showed that the control algorithm is effective for peak load limitation, the maximum demand value reached during simulations tests did not exceed the preset demand limit at any time interval. The performance of the algorithm could be improved when prioritizing the shutdown of loads with higher nominal power or when increasing the anticipation time used for the disconnection of the controllable loads.

Optimization of an Offshore Oilfield Multi-Platform Interconnected Power System Structure

In recent years, with regional integrated seabed resource exploitation continuing to expand, offshore oilfield power systems have gradually become networked, forming an offshore oilfield multi-platform interconnected power system. In this paper, a two-level optimization model for offshore oilfield multi-platform interconnected power system structure is developed. The lower-level model uses a minimum cut-load cost model that considers load priority to calculate the power outage losses of all N-1 fault conditions. The reliability assessment method helps to achieve a balance between economy and reliability. Additionally, an optimization model simplification method based on graph theory is proposed, and it uses the minimum cost maximum flow (MCMF) algorithm to optimize the power flow of the grid and narrows the problem's solution space. At the end of modeling, a parallel global taboo search algorithm is used to reduce the computational time and improve the probability of finding an optimal solution. Investigations of a real offshore oilfield multi-platform power system verify the accuracy of the model, the simplification method and the parallel global taboo search algorithm.

Multi-Agent System based Two-Phase Market Model to Incorporate Demand Response in Grid-Tied Microgrids

In this paper, a multi-agent-based intelligent energy management framework (MEMF) is proposed to manage power balance in grid-tied microgrids. The MEMF maintains two virtual markets viz. Local market in which potential sellers and buyers will participate in trading within the microgrid using Continuous Double Action (CDA) market auction and Global market in which power mismatch (surplus/deficit) of a microgrid in the local market is mitigated by the grid. In addition, a novel linear bidding algorithm is introduced for stakeholders to decide their quote prices for day-ahead trading intervals for ethical trading. The trading mechanism is modified dynamically to enhance benefits to both sellers and buyers. It also enables customers with low priority loads to participate voluntarily in demand response(DR). The generally used incentive policy is also modified to yield more benefits to the customers. In the proposed MEMF, both Multi-Agent System (MAS) and grid-tied microgrid are simulated using MATLAB/SIMULINK. The simulation results on a test system are presented for illustrating the effectiveness of DR on the proposed trading and managing algorithm.

Determinations of Annual Investments of DisCo and Private Sector in Expansion Planning of Distribution Network

Ever-increasing load-demand, threaten the continuity of power supply and diminishes power quality causing serious technical challenges in distribution networks. In this regards, commonly used cheaper practices like capacitor allocation and network configuration, can enhance operational parameters of the network. On the other hand, supplying the future loaddemand, requires installing new substations or distributed generations (DGs). Because of DGs inherent technical and economic advantages, Distribution Company (DisCo), encourage private investment to install DGs, by signing long-term Power Purchase Agreement (PPA) with DG owners (DGO). In such a situation created, DG allocation and simultaneously capacitor allocation and network configuration can be economical for both DisCo and DGO and also can enhance the reliability and power quality. So, this paper is aimed to find the annual instants for network configuration; timing, sizing and sitting of capacitors and DGs, and PPA rates using Multi-Objective Particle Swarm Optimization (MOPSO). Also, the uncertainties of load-demand and electricity-price, voltage-sensitive loads and load priority in power supply are applied into the planning problem. Voltage deviation and voltage stability are considered as constraints which must be better in optimum solution. The effectiveness of the proposed scheme, is validated by computer simulation done on a 33-Bus IEEE distribution network and the outcomes are discussed.

An improved energy management method based on combination of multi agent system and fuzzy logic

This paper presents a distributed energy management strategy based on a combination of multi agent system (MAS) and fuzzy logic (FL). Our system is composed of two renewable energy sources which are a photovoltaic panel, wind turbine, storage system and a variable loads. These elements are linked between them through switches. This improved technique allows the control of switches to manage the energy flow between sources and established load priority. MAS consider each element of the system as an agent, one of them is called execution agent that controls switches based on the paradigm of fuzzy logic (FL). The results of this approach are presented, discussed then followed by a simulation.

An Interactive Partitioning Algorithm-Based Electrical Power Crisis Management for Service Restoration with Existing Black-Start Resources Considering Load Priority

This article explores a new clustering-based solution for power systems that would be restored by post-outage power supply. The suggested solution has divided the overall network into separate clusters in such a way that each cluster has at least one ‘black-start unit,’ which can restart its own generation. The proposed algorithm manages a variety of restrictions when creating clusters, such as black-start generation capability, supply–demand balancing, and load priority, and attempts to minimize the active and reactive power difference between black-start unit and cluster loads as well as minimize power losses. This paper focuses on the standby available sources in a network to act like ‘black-start units,’ while their neighboring high-priority loads as defined by the supply provider were called cluster participants. K-means neighbor clustering algorithm was used to form the clusters, and, before validating each cluster, the steady-state voltage stability of the clusters was tested using equivalent network L-index value analysis technique. Two separate case studies were performed on the modified distribution IEEE 33-bus system and New England electrical transmission system with 39 buses to demonstrate the efficacy of the proposed algorithm. The results of the simulation show that each cluster was consistently able to maintain reasonable voltage stability with minimum power imbalance between available BSU and loads.

A new opposition crow search optimizer-based two-step approach for controlled intentional islanding in microgrids

Crow search optimizer is considered as the latest meta-heuristic algorithm that is influenced by crow’s behavior. The proposed oppositional crow search optimizer (OCSO) is intended here, for solving the intentional islanding problem. This paper has proposed a novel two-step method by considering the most significant factors such as the constraints of line capacity, bus voltage, load priority, load controllability, problem occurred due to spacing of solutions as well as the capability of integrating the islands to produce higher intentional islands. Initially, the tree knapsack problem is considered as an intentional islanding issue and therefore the OCSO algorithm is employed in solving such shortcomings. In OCSO approach, the opposition-based generation jumping and population initialization concept are used in crow search optimizer for improving the convergence profile and computational speed. In next process, island feasibility is verified by means of conducting power flow computation and providing significant modifications. Six distributed generations containing IEEE 69-bus test system are employed in validating experimentally the efficiency of the proposed approach, and comparison was done for the obtained results with the other existing approaches. The comparative analysis is evaluated to enhance the level of reliability, particularly critical load.

Distribution system restoration using graph theory after multiple faults

This study presents a novel graph theory-based approach to restore the distribution systems (DSs) after multiple simultaneous faults due to extreme weather conditions. The algorithm promptly finds the switching sequence to energise as many healthy zones as possible in an unbalanced DS. All the resulted topologies are later checked for operating constraints such as line flows and bus voltages. Hierarchy based load curtailment is proposed to remove any existing violations by shedding minimum non-critical loads. The proposed algorithm also gives the order in which the faults have to be cleared, depending on the priority of the disconnected load and inconvenience caused to the public. OpenDSS is used for unbalanced power flow analysis. Studies on the IEEE 37-bus system and the IEEE 123-bus system compared the proposed algorithm with other existing methods in the literature.

Decentralized transfer of contingency reserve: Framework and methodology

The increasing penetration of renewable energy comes with decreasing system inertia and much faster frequency drop when contingency of large power loss occurs, which seriously threatens the security of power system operation. Meanwhile, the conventional contingency reserves will be of serious shortage and unable to satisfy security requirements in the future. To solve these problems, the concept of decentralized transfer of contingency reserve (DTCR) has been recently proposed to partially transfer the centralized contingency reserve from the supply side to the demand side, attempting to realize smart decentralized reserves with higher security and lower cost. To continue this work, this paper further elaborates the methods of implementing DTCR. Firstly, the framework of the DTCR system is formulated. Then, a refined load frequency control for contingencies is developed with millisecond-level speed and appliance-level control accuracy. The proposed three-stage control strategy is composed of instantaneous conservative response (ICR), adaptive latent response (ALR), and optimal dynamic control (ODC). As the basis of all responses, an estimation method of the range of power imbalance and frequency nadir is given, considering communication mechanism and parameter errors. In the ICR, a communication-free active response scheme is proposed considering load priority and magnitude to achieve rapid nadir control, and the setting formula of conservative response capacity (CRC) to avoid unacceptable low frequency by reliable minimum load resources is presented for the first time, which can mitigate the adverse impact caused by mis-shedding and enhance the error-tolerance. In the ALR, an online adaptive correction method is presented for key parameters to achieve accurate frequency restoration and decrease the impact of uncertainties in the sliding time window. Finally, the effectiveness of the proposed DTCR realization method is demonstrated through the simulation on a modified small-inertia IEEE 14-bus system with wind power penetration. Further tests indicate the ICR and ALR possess high security performance in the frequency control for handling contingencies.

A Generalized Framework for Service Restoration in a Resilient Power Distribution System

An electric power grid is one of the complex infrastructures, and because of its complex nature, there is simply no way that outages can be completely avoided. Thus, a modern society that depends on reliable electric supply requires a resilient electric system that can recover from disruptions while integrating emerging smart grid technologies. This article presents a novel approach for service restoration in a modern power distribution system with controllable switches and distributed generation (DG) resources for any kind of outage. The proposed framework supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islands that are dynamically sized using algorithms based on the fault scenario, available resources, and priority of loads. The problem is formulated as a mixed-integer linear program that incorporates critical system connectivity and operating constraints. Simulations are performed to demonstrate the effectiveness of the proposed approach using a large-scale four-feeder 1069-bus three-phase unbalanced distribution test system. It is demonstrated that the framework is effective in utilizing all available resources in quickly restoring the power supply to improve resiliency during extreme events and is scalable for a large-scale unbalanced power distribution system.

A matheuristic framework for the Three-dimensional Single Large Object Placement Problem with practical constraints

The Three-dimensional Single Large Object Placement Problem consists of a set of weakly heterogeneous items that must be placed inside a single larger object without overlapping each other. Many constraints can be considered depending on the practical specifications of the problem being solved, such as orientation, stability, weight limit and positioning. Although this is a well-known problem which has received considerable academic attention, most of the research limits itself to considering only three basic constraints: non-overlap, orientation and stability of the placed items. Recent literature concerning the problem has indicated that there is a pressing need for solution methods which consider a more realistic number of sets of practical constraints given that it is very uncommon to find real-world situations where only a few of these constraints are considered together. Therefore, this paper introduced a well-performing matheuristic framework which considers multiple practical constraints: orientation, load balance, loading priorities, positioning, stability, stacking and weight limit. An extension of the developed method considering multiple containers is also discussed. Results are compared against the state of the art from the literature and demonstrate the robustness of the proposed matheuristic with respect to different combinations of constraints.

Design of a Smart Energy Management System for Photovoltaic Stand-Alone Building

PV stand-alone buildings, that depend completely on PV array, are suffering from the uncertainty of the supplied power due to weather conditions. To manage this uncertainty and reduce the risk of completely losing power, the proposed system offers a design that able to manage the consumed power according to the produced energy, battery capacity, and weather conditions, it also gives permission for the user to select its load priorities. A hardware prototype has been built to simulate all the system possible states included; PV voltage & current, the battery capacity, PV produced power, the loads. An ESP32 microcontroller has been used to adapt accurate forecasting and loads management strategy with a prediction model for managing the consumed energy with hourly and two days ahead planning. The results show a promising module that able to manage the stand-alone building energy without affecting the quality of life.

Integrated Energy Load Priority Scheduling Model Based on Customer Satisfaction

The integrated energy system is of great significance to improve the efficiency of social energy utilization, promote the large-scale consumption of renewable energy and the cascade utilization of energy. In view of the uncertainty of cooling, heating and electric load demand of users, this paper proposes a load priority supply model for integrated energy service providers, which considers the user satisfaction and the penalty function of energy supply interruption, and is used to deal with the scheduling optimization of various uncertain energy demand and translatable load. The optimal scheduling mathematical model of community integrated energy system is established. The various optimization objectives and the constraints of different energy demand are considered, the heuristic algorithm is used to solve the optimal scheduling, and the economic of operation mode is analyzed. The model can effectively reflect the impact of energy demand, user satisfaction and system cost on the priority of supply load, and provide the optimal decision to weigh the system cost and user satisfaction. The simulation results show the feasibility and effectiveness of the proposed model.

Automatic Load-Shedding Protection at a Coal-Chemical Plant: Examining and Designing Effective Low-Frequency Backup Protection Methods

This article examines and studies a coal-chemical plant through a dynamic computer model as well as designs an automatic load-shedding protection. The plant has on-site generation to support partial plant loads. In the past, disconnecting from the utility grid had caused frequency and voltage decays in the plant electrical system to dropout critical loads. A complete system computer model is constructed, including generator dynamics, excitation/automatic voltage regulators (AVRs), and prime mover/governor control characteristics. Transient stability simulations of various loading and generation conditions were carried out to understand and study system frequency and voltage behaviors and patterns of change under a sudden islanding case. This was accomplished by analyzing frequency versus time and rate of change of frequency versus time curves. Further, through carefully evaluating plant load priorities as well as load dependencies, a feasible load-shedding control scheme with backup strategies was configured and simulated by computer modeling and study. Simulation results verified that the proposed loadshedding scheme can correctly respond to frequency drop and automatically initiate the shedding of noncritical and independent loads to prevent further system frequency drop, thereby exceeding the allowable deviation. The suggested solution is automatic and feasible and has been successfully implemented at the plant.

An Adaptive Underfrequency Load Shedding Scheme in the Presence of Solar Photovoltaic Plants

Load shedding is performed, as last option, for emergency measure when system frequency drifts below the allowed limit. Utilities use underfrequency load shedding schemes to bring back frequency within limit. In this article, an adaptive underfrequency load shedding scheme is proposed for the power system in the presence of solar photovoltaic plants using wide-area measurements. Stepwise load shedding is performed at selected buses with consideration of any change in photovoltaic power output and load/generation at each step. Share of load to be shed at a bus is based on the voltage stability index of the buses at that instant considering predefined load priority. The voltage stability index at a bus is obtained using the Thevenin equivalent of the system. For a solar photovoltaic plant integrated system, the Thevenin equivalent changes with solar power variation, where available methods become inaccurate. A Thevenin equivalent calculation approach is proposed in this article using an equivalent model of synchronous generators and photovoltaic plants. The proposed load shedding method is tested on the New England 39-bus system and the 233-bus North Eastern Indian grid with integrated photovoltaic plants. It is compared with available methods and found to be a better technique in bringing system frequency to normalcy with adaptive load shedding.

Optimal planning of multi-micro grids based-on networks reliability

Traditional electrical grid as the largest and most complex industry in the world is currently under fundamental development. According to the IEEE Std1547.4, large distribution systems can be clustered into a number of micro-grids (MGs) to facilitate the control and operation infrastructure in future distribution systems. A distribution network with distributed energy resources serving a group of loads, can be operated as a MG, in grid-connected or isolated grid modes. Most recently, the concept of MGs has become an important subject in smart grid area, demanding a systematic method for their optimal planning. The prosperous development of the micro-grid concept implies the definition of an appropriate regulation for its integration on distribution systems. The MG operators are responsible for a reliable energy supply to their consumers. MG formation represents a suitable solution for uninterrupted supplying of critical loads, in the absence of network.In this study, a systematic approach is presented for optimal construction of micro-grid. The problem is solved in two stages. In the first stage, the network is designed as an integrated and active distribution network considering the importance of reliability by using Backward–forward load flow. Next, the distribution network is designed to be divided in to several MGs in order to minimize the cost of electricity generation, improve reliability and voltage profile. In the presented study, stochastic models are considered for representing generation availability related to wind energy and solar energy. The generated power of distributed generation resources such as wind turbines and photovoltaic, have been modeled per hours of the day and the amount of important network parameters are obtained from the results of probabilistic load flow. Practical and important factors such as problem solving space reduction, load controllability, load priority, bus voltage limitation, line capacity limitation and the possibility of forming larger micro-grids by connecting switches between the obtained initial MGs from the first stage are considered in the proposed method.The proposed methodology is implemented for a standard 69-bus distribution system by applying the Imperialist Competitive Algorithm (ICA) in MATLAB software. Results verify the usefulness of the proposed approach in transforming an existing radial distribution network into several autonomous MG.

An intelligent load priority list–based integrated energy management system in microgrids

In contrast to most contemporary literature on demand-side management (DSM) in microgrids (MG), which often neglects the granularity of the load importance degree prior to the shedding time or at best arbitrarily fixes a certain load priority list (LPL), this paper introduces spatially and temporally varying LPL-based DSM that determines the load category and corresponding amount that should be curtailed. The high penetration of the intrinsically stochastic renewable resources in MGs elevates some reliability problems and supply–demand balance instabilities in MGs, raising the necessity for applying robust and prompt demand response (DR) actions to avoid purchasing expensive energy from the grid, especially during peak hours. The proposed neuro-fuzzy LPL-based DSM is developed and implemented to offer smarter and less severe DR actions that will ensure a minimised operational cost, without shedding critical or essential loads. By applying the methodology on an MG testbed with real and local multi-category demand and hybrid renewable generation data, the results showed the effective optimization of the MG self-generation adequacy through DSM, especially during grid’s high-tariff periods.

Fuzzy logic based multistage relaying model for cascaded intelligent fault protection scheme

Designing an instrument protection system has been considered as one of the most challenging domains in the power system. Intelligent tripping mechanism has been introduced in few sectors for controlling the moving contacts of a single operating relay connected to an electrical system. In this work, we have developed a cascaded relaying for a priority based decision making fuzzy inference system with fault tripping mechanism for controlling multiple operating relays. To obtain this priority based design of intelligent relaying, a multistage cascaded relaying is considered for achieving the higher accuracy. In this paper, we have discussed regarding the simulation of a load priority based tripping mechanism incorporated intelligent relaying. The differential equation achieved from the stage - I relaying simulation model of measurement blocks and fault generator blocks have been discussed. We have validated the stage - I relaying of the entire concept of the design with the mathematical modeling and also the simulation results of the stage - II output. The proposed method utilizes the concept of two stage relaying by introducing a Fuzzy Inference System designed to control the isolator state for the particular multistage based cascaded intelligent fault protection scheme.

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

Demand management by active consumers in intelligent electric power systems

The approaches to demand management by active consumers through forming a given schedule of power consumption in the required period of time based on the solution of the optimization problem in the form of maximizing the power of connected controlled electric receivers of various types are presented. The model of demand management by active consumers is justified, taking into account the following factors: load sensitivity for connecting a transformer substation to a change in consumer load; load priority; consistent load reduction levels with flexible performance and power control; permissible set of electric receivers in accordance with the technological process, network schedule, other logical conditions corresponding to adjacency lists. An algorithm has been developed for limiting power on the part of active consumers based on the widespread use of digital data processing technologies, modern technical means of measurement, control and switching of end consumers in real time. The presented research results indicate the validity of the demand management method by active consumers in the normal mode of intelligent electric power systems and the possibility of its practical implementation in an industrial enterprise with reference to the technological process.