Non-critical Loads Research Articles

Distributed Adaptive-MPC Type Optimal PMS for PV-Battery Based Isolated Microgrid

In the absence of an external grid, supplying quality power to critical loads in renewables, i.e., solar photovoltaics (PV)-battery-based self-sustained isolated microgrid (MG) system is quite troublesome. Because of involved generation arbitrage and variable load conditions, a robust power management scheme is essential. In this article, a distributed strategy with an adaptive model predictive control (MPC) principle is proposed. It considers all possible uncertainties, viz., intermittent generation as time-varying solar irradiance, variable load profile, alongside maintaining smooth charging/discharging cycle of storage units and shedding of noncritical loads. For this purpose, a novel comprehensive small-signal state-space model of MG with PV-battery systems having dedicated parallel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLC</i> loads and a low-voltage network is considered, which accounts for inverter dc-bus link dynamics. The controller tracks the maximum power point of PV and keeps the MG power balance intact for both active and reactive powers by considering interconnected bus dynamics. The viability of the proposed scheme is verified by performing extensive testing, viz., variable solar irradiance profile, continuous small and large variations of loads, shedding of the noncritical loads, multiple topology subsystems, comparative study with state-of-art MPC, and smooth transition of battery charging/discharging cycles using MATLAB platform over 24 and 48 h duration.

Deployment of IoT-Based Smart Demand-Side Management System with an Enhanced Degree of User Comfort at an Educational Institution

Nowadays, the Internet of Things (IoT) has a wide impact on many potential applications. The impact of IoT on performing demand-side management (DSM) in an Indian educational institution has not been researched in depth before. In this research work, an IoT-enabled SDSMS (Smart DSM System) has been deployed with the main objective of minimizing electricity tariff and also to tweak the quality of user comfort. It can be feasible by prioritizing available renewable PV solar energy during peak hours in an Indian educational institution. DSM has been performed using day-ahead load shifting and rescheduling the different classes of institutional loads by applying hybrid BPSOGSA (Binary Particle Swarm Optimization and Gravitational Search Algorithm). The BPSOGSA performance on DSM has been evaluated based on electricity tariff, peak demand range, and PAR and compared with the outcomes of both binary conventional algorithms BPSO and BGSA, respectively. The proposed method enhances the degree of user comfort (DUC) by tripping the operation of non-critical institutional loads. Simulation results obtained using MATLAB corroborate that BPSOGSA outperforms both BPSO and BGSA under both DSM scenarios. Before DSM, Peak demand, PAR, and Electricity tariffs were found to be 1855.47 kW, 4.1286, and $2030.67 while after DSM, they reduced to 1502.24 kW, 3.263, and $1314.40 respectively. This indicates a 35.273% reduction in electricity tariff, a 19.037% scale down in peak demand, and a 20.97% reduction in PAR. Finally, the real-time IoT-based SDSMS hardware is implemented at the Renewable energy laboratory for real monitoring of energy consumption via the Blynk application.

A Resilient Prosumer Model for Microgrid Communities with High PV Penetration

Worldwide energy shortages and the green energy revolution have triggered an increase in the penetration of standalone microgrids. However, they have limited generation capacity and are wasteful when excess generated energy is curtailed. This presents an opportunity for the coordinated operation of multiple prosumer microgrids that absorb this waste to enhance their resiliency and reliability. This paper proposes a reliable prosumer model with an inbuilt energy management system (EMS) simulator that considers the mentioned deficiencies for constructing resilient interconnected prosumer microgrids. The EMS simulator operates in a real-time dynamic environment to coordinate the prosumer components and performs flexible switching for (1) prioritizing critical load by shedding the non-critical load and (2) meeting load demand locally or from other interconnected prosumers. The EMS simulator maintains energy balance by setting limits for the battery energy storage system (BESS) to preserve energy during low generation and performs real-time monitoring. The novelty of this model lies in its simplicity and flexibility, which allows interconnected prosumer microgrids to operate in cooperation without the need for communication. The proposed model is evaluated in a post-disaster off-grid scenario using a single-phase average value model that considers reasonable computation time. The simulation results of the proposed system indicate the preservation of stored energy while maintaining critical resources beyond three days of poor weather conditions.

A Price-Based Strategy to Coordinate Electric Springs for Demand Side Management in Microgrids

Electric springs (ESs) have proven effective for integrating renewable generation into power systems. An ES connected in series with a non-critical load forms a smart load whose consumption can be dynamically controlled for voltage regulation and demand side management. In most existing applications, smart loads have been devoted to providing services to the grid without accounting for their own interests. The novelty of this paper is to propose a price-based strategy to coordinate the operation of multiple ESs in microgrids. Smart loads consisting of ESs connected to electric water heaters are modeled as rational agents that locally optimize their own objectives by adjusting their consumption schedules in response to price/control signals. Such signals are determined at the microgrid central controller (MGCC) when solving the microgrid operation scheduling problem formulated to minimize the microgrid operation cost taking into account the smart loads’ consumption schedules. An iterative optimization algorithm determines the equilibrium between the microgrid and smart loads’ objectives requiring only the exchange of price/control signals and power schedules between the local controllers and the MGCC. Case studies show the effectiveness of the proposed strategy to economically benefit both the microgrid and smart loads when scheduling their operation.

Small Hydro Based Grid Forming Converter Having Power Sharing and Synchronization Capability With DFIG Based WECS

In the presence of the utility grid, the inverter based resources such as wind, hydro and photovoltaic operate in the grid following mode, as the voltage and frequency are assigned by the utility grid. However, in remote areas, where the presence of grid is challenging, it is required to maintain stiff voltage and frequency at the point of common interconnection of loads in standalone mode. In this work, a unique configuration comprising doubly fed induction generator (DFIG) based wind energy conversion system (WECS), small hydro-solar photovoltaic (SH-PV) based hybrid microgrid is presented, where the local grid is formed by the SH-PV system combined with a battery energy storage (BES). Switching logics are developed for the small hydro based grid forming, machine side and load side converters in such a way that synchronization of the WECS with the SH-PV is smooth. Besides, active filter operation of the load side converter is also demonstrated. The proposed microgrid is tested under various transitional modes and gives an insight of interactions between these inverter based renewable energy sources. The hybrid microgrid presented depicts bidirectional flow of power. This way uninterrupted power is supplied to the local loads and also supports noncritical loads. The work concludes with a laboratory prototype to illustrate the operation of hybrid microgrid in numerous modes.

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

Defining a new index to compare the resilience of different structures of an electrical energy network

The issue of resilience in electrical distribution systems has been proposed increasingly with the frequent occurrence of natural disasters in recent years and the imposition of high costs due to widespread power outages. To date, various resilience indices have been proposed, some of which have been improved upon over time by extensive research, leading to more comprehensive indices. However, a standard index has not yet been approved and presented in this regard by international committees, despite the efforts made. The issue of resilience in electrical networks was examined by curve analysis index in more detail compared to other proposed indices, although it is not yet complete and should be investigated from various aspects and its shortcomings be eliminated. The present study aims to evaluate some fundamental obstacles in the “curve analysis” index and correct it in a new index called “Combining Investment and Reform” (CIR) index. Two issues of “costs in terms of investment and repairs imposed by the event” and “need to separate critical and non-critical loads” are considered simultaneously in the proposed index. Finally, the capabilities of the proposed index are evaluated and compared in a sample electrical network in the face of events with different intensities.

A Proactive Microgrid Management Strategy for Resilience Enhancement Based on Nested Chance Constrained Problems

As the frequency of extreme weather events rises, the resilience of power systems is becoming increasingly important. This paper proposes a proactive microgrid management strategy for enhancing the resilience of microgrids (MGs) based on nested Mixed Integer Linear Programming problems with chance constraints. In the proposed method, MGs operate in a special operating mode referred to as the “preparation mode” to protect the vital load and maximize resource operation efficiency when an external grid outage warning is issued. The preparation mode problem is formulated to reflect both the normal and emergency mode operation conditions. The on-event phase-operation problem under emergency-mode operation conditions is nested within the pre-scheduling problem under normal-mode operation conditions in the preparation mode. Further, according to their importance, loads are divided into critical and non-critical ones in the problems. The former is represented by a chance constraint, and the latter is represented by the expected cost of load shedding in the cost function. The numerical examples demonstrate that the proposed preparation mode enables the MG to guarantee a high chance that the critical load will survive and to lower the cost of the non-critical load shedding with a minor increase in resource operation costs.

Distributed Virtual Inertia Implementation of Multiple Electric Springs Based on Model Predictive Control in DC Microgrids

Smartload with series-connected dc electric spring (ES) and noncritical load (NCL) structure can compensate for load voltage and improve the power quality. In this article, a distributed virtual inertial control framework based on model predictive control is proposed for low inertia in the dc microgrid. First, the current prediction model of the ES bidirectional full-bridge dc/dc converter is presented. Based on the virtual inertia control, model predictive control is used to optimize virtual capacitance during the operation. When the system suffers from disturbance, virtual capacitance increases to slow down the change of the dc bus voltage. When disturbance happens, the system can be stabilized quickly by reducing the virtual capacitance. Then, the consensus algorithm based on distributed control is established after defining the NCL voltage deviation rate, which can achieve the balance of NCL voltage deviations, and effectively avoid the more significant voltage deviation in one of NCLs. Furthermore, the small-signal model of the proposed control method is developed, and the influence of the proposed controller on the small-signal stability is described by employing the eigenvalue analysis method. Finally, based on the RT-Lab hardware-in-the-loop simulation system, comparisons and analysis are made to verify the effectiveness of the proposed control method under power step conditions.

Probabilistic Rolling-Optimization Control for Coordinating the Operation of Electric Springs in Microgrids With Renewable Distributed Generation

Electric spring (ES) is a novel smart grid technology developed to facilitate the integration of renewable generation by controlling the demand of non-critical loads (NCLs). The utilization of ES to provide a single service such as voltage or frequency regulation, validated in a setup consisting of a single ES, has been extensively investigated. However, to take full advantage of this technology, it is necessary to develop control strategies to coordinate the operation of multiple distributed ESs to provide multiple services in power systems. To this end, this paper presents a rolling-optimization control strategy to coordinate the operation of multiple ESs for voltage regulation, congestion management and cost minimization of the real-time deviations from the scheduled energy exchanges with the grid in microgrids with renewable generation. The strategy is for centralized implementation, and includes a probabilistic optimal power flow-based optimization engine that finds the voltage references of ESs for each control interval taking into account generation variability and uncertainties. NCLs consist of electric water heaters, which are modeled taking into account physical constraints and the hot water demand. Simulations were carried out in two test systems with 14 and 33 buses.

Research on electric springs with switchable smart load

In the electric spring (ES) topology, traditional smart load (SL) consists of non-critical loads (NCL) and ES in series to regulate power and stabilize the voltage of critical load (CL). Aiming at the problem that NCL cannot always obtain stable voltages, this paper derives the circuit diagram of ES and CL in series to form new SL to stabilize voltage of NCL through circuit transformation. The new SL combines with the traditional SL to form switchable SL. The circuit topology of switchable SL is deduced, and the basic principle of switchable SL is analyzed. The control strategy of ES with switchable SL is studied. The simulation experiment results in Matlab/Simulink show that the voltage of different loads can be stabilized by controlling ES with switchable SL in different time periods.

An Electric Spring Without Noncritical Load Based on Fractional-Order Components

Electric springs (ESs) have been proposed as a new emerging distributed demand-response technology that can be used to solve the problem of voltage fluctuations caused by intermittent renewable energy power generation. In order to ensure the voltage stability of critical loads (CLs), most of ESs are connected in series with noncritical loads (NCLs) so that the transferring fluctuating energy can be transferred to NCLs. However, the effective operating range of ES is limited by the voltage withstood by NCL. In this article, after analyzing the influence of NCL on the operating range of ES, a fractional-order ES is proposed, which can regulate the voltage of CL without NCL. The proposed ES is based on the characteristic that fractional-order components can consume or release active power under different orders. Topology, operating principle, and voltage regulation range of proposed fractional-order ES together with control method are detailed. The effectiveness of fractional-order ES has been verified via simulation and experimental results.

A Shunt DC Electric Spring-Based Control Strategy for Real-Time Critical and Noncritical Load Management in DC Microgrid

Deployment of DC Electric Spring (DCES) technology is an innovative demand side management (DSM) program that helps to mitigate voltage interruptions. Conventional control strategy uses proportional-integral controllers to regulate DC spring parameters. As PI controllers have a trade-off between settling time and peak overshoot, they may not be able to achieve the desired dynamic performance under varying DC microgrid (DCMG) conditions. In this regard, this paper proposes an inverted zero compensator for the control of shunt DCES (ShDCES) in DCMG. This controller regulates the ShDCES current and terminal voltage under all operating conditions with reduced settling time and peak overshoot. A power management algorithm is also proposed to optimize the operation of ShDCES under the presence of varying PV generation and critical and noncritical loads. A detailed mathematical analysis of ShDCES and inverted zero compensator design are furthermore incorporated to validate the strategy. The simulation of the proposed method is performed in MATLAB/Simulink environment. The comparison analysis with the conventional controller proves the effectiveness of the new controller. The results are also validated using real-time simulator OP4510RTS.

An Improved Electric Spring Topology Based on LCL Filter

Electric spring (ES), which has been widely discussed in recent years, is a new demand response method. By adjusting the power of noncritical load (NCL), ES can adaptively improve power quality of the grid containing a high proportion of renewable energy sources. Most of the existing ESs is connected in series with NCLs, but this structure limits the compensation power range of ES. In this article, a new ES topology based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filter is proposed, where NCL is paralleled with the capacitor branch as the shunt damper. Compared with other types of ES, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -ES has the advantages which can extend the adjustable power range, improve the voltage regulation performance, and suppress the inherent resonance spikes effectively. Finally, the feasibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -ES has been verified by simulation and experimental results.

Enhancing distribution system resiliency with microgrids formation using weighted average consensus

Integration of distributed energy resources (DERs) including renewable and storage, digital automation and the advanced metering infrastructure (AMI) provides an opportunity to further enhance resiliency during outage of main feeder. Access to local DERs with enhanced control ensures the sustainability of energy continuity to urban critical (vital) loads, by forming DER-based microgrids. Microgrid formation is challenging considering detecting failures; priority of loads; detecting location and boundary of load outages, while meeting the energy constraints and data sharing limitations to maximize resiliency. Consensus algorithms provide an opportunity to share data in limited manner but still allowing to reach consensus on the resource and network control for microgrids formation and enhance resiliency. In this work, a weighted average consensus mechanism has been proposed to detect failures due to faults and/or data loss and, hence, determine the isolated nodes as a first layer. Additionally, a mixed-integer linear programming (MILP) optimization problem is formulated and solved as part of the second layer to form the DER-based microgrids with the goal to improve the resiliency of the distribution system, while satisfying the consensus-based constraints. The proposed method is evaluated using the standard IEEE 123-node feeder. Simulation results demonstrate the effectiveness of the proposed weighted average consensus method in enhancing the distribution system resiliency by detecting failures and node isolation; and forming DER-based microgrids considering critical and non-critical loads.

Demand response considering user behaviour differences for load serving entity: A multi‐agent deep reinforcement learning approach

Demand response can reshape the load profiles by adjusting the energy price to improve overall performance of power grids. For the load serving entities (LSEs), the optimal demand response shall satisfy operation constraints and gain maximised profits. However, the individual end-consumer will surely value electricity differently, and price adjustments without considering the differences of user behaviours may induce unbalanced dispatch and degrade the overall profits. Given affordable price ranges under which the non-critical loads can properly response to price signals, the demand response can be operated in a manner where non-critical loads at different areas coordinately adjusted to achieve the global dispatch objectives. For this purposes, this paper proposes a cooperative demand response approach for LSE pricing based on a multi-agent deep reinforcement learning approach. The learning approach constructs the distributed pricing agents that cooperatively determine the price signals to be responded by different load clusters. To update the parameters of pricing agents, the deep deterministic policy gradient is derived considering the constraints and different behaviours of load clusters. The effectiveness of the proposed cooperative demand response method is verified based on numerical simulations.

A subjective one-item measure based on NASA-TLX to assess cognitive workload in driver-vehicle interaction

This research introduces requirements for subjective instruments that assess the cognitive workload of in-vehicle information systems (IVIS) while driving. The suitability of 11 existing subjective instruments is evaluated, but none of the instruments matches all of the proposed requirements for this specific scenario. Therefore, a new subjective one-item measure is presented. The one-item measure is based on the mental dimension of the NASA Task Load Index (NASA-TLX) questionnaire. It combines the discussed requirements for IVIS research, e.g., time-efficiency and descriptiveness for non-expert participants. A series of three studies with 107 participants overall was conducted to verify the suitability of the measure. The results show that the instrument discriminates effectively and is valid between different levels of cognitive workload. The results are stable across different experimental setups and samples, and the instrument shows high sensitivity even in non-critical cognitive load levels.

Linear Matrix Inequality Approach for Robust State Feedback Control of Electric Spring: A Device for the Real Time Demand Side Management

Ever-Increasing environmental concern has raised the penetration of renewable energy sources in the power grid, which brings instability to the grid due to its intermittent nature. Electric Spring (ES) was proposed as the decentralized mechanism to bring voltage regulation and stability to the grid, when being installed at the customer’s end. This work focuses on the control aspect of the ES, being used for providing voltage regulation to the critical load by manipulating the voltage across the non-critical load, amidst fluctuating voltage inflicted by renewables, through injecting voltage with appropriate magnitude and phase. State-Feedback control of ES has been designed using the Linear Quadratic Regulator, being optimized using the numerical convex optimization called Linear Matrix Inequalities, to furnish robustness to the control with optimal control effort. The Cyclic Design approach has been used to transform MIMO system into SISO system, to provide simplicity to the controller design without sacrificing its robustness and efficacy. Integrator has been introduced there in, to bring the steady-state error to zero. The superiority of the proposed control has been established through comparison of results derived through this controller with that of a PI controller, which is widely used for executing the control of ES.

Analysis and Design Methodology of a Novel Integration Topology of Storageless Off-Grid PV Systems

In this manuscript, a novel topology is proposed to integrate on-grid and off-grid PV systems supplying the same load premises. The load side is investigated and analyzed, and critical and non-critical loads are separated in terms of power supply. Critical and seasonal loads usually place stress on the grid’s point of common coupling (PCC). In the proposed topology, they are supplied by a novel topology for an off-grid solar pump PV system that lacks energy storage integration. The lack of energy storage batteries requires a novel design and sizing scheme for the off-grid PV system, and a methodology is proposed in this manuscript. The on-grid PV system is conventionally designed and coupled with the storage-less off-grid PV system to maintain load supply. The proposed methodology minimizes the ratings of the PCC and hence relieves stress on congested grids with renewable energy penetration, especially in condensed urban areas. The proposed structure enables the operation of microgrids with high penetration levels of renewable energy resources and minimizes dependance on storage batteries for off-grid systems. A case study is presented and thoroughly analyzed with the proposed methodology. The outcomes of the design process are evaluated economically and were found to be feasible, as is detailed and supported with simulation results.

Optimizing the operation of energy storage embedded energy hub concerning the resilience index of critical load

Dealing with high-impact events in energy hubs requires increasing resistance and restoration capability in multi-carrier energy systems. Assessing resilience as an essential and high priority measure must be done to ensure the optimal and economical operation of the energy hub. Thus, this paper introduces a cost-based optimization model of the energy hub that contains storage and considers resilience constraints and other general constraints. In this methodology, loads are categorized as critical and non-critical. The approach defines the highest priority for supplying the critical loads, whereas interruptions in non-critical loads must be minimized. That in this context, storages play a vital role. Four scenarios are investigated as normal situations and contingency cases. The contingency scenarios examine energy carrier interruptions such as electricity, gas, and heat input in an energy hub system. The mathematical model of this problem is founded based on a Mixed Integer Nonlinear Programming (MINLP), which DICOPT solver implements in GAMS. Mentioned model is evaluated by simulation of different case studies for the given energy hub test system. The trade-off between increasing critical load resiliency and reducing total costs is investigated. The results show that as critical load flexibility increases, total costs increase. In the worst-case scenario, only up to 13% of the total load can also be considered as critical load. Moreover, in different scenarios, the results demonstrate that using storage devices helps to increase resiliency Furthermore, in severe events, using storage devices has an increasing trend.