Microgrid Demand Research Articles

Managing the Demand in a Micro Grid Based on Load Shifting with Controllable Devices Using Hybrid WFS2ACSO Technique

Demand Side Management (DSM) is an effective tool for utilities through reducing the demand of peak load and controlling the utilization of the energy of the system. The implementation of DSM provides benefits for utilities and is profitable for the customers who are involved in this process. DSM based on a load shifting strategy is proposed in this paper by employing various devices to minimize the energy consumption pattern in the system. The proposed hybrid strategy is the joint implementation of the Wingsuit Flying Search Algorithm (WFSA) and Artificial Cell Swarm Optimization (ACSO). The searching behavior of WFSA is enhanced by ACSO. Hence, it is named the WFS2ACSO technique. The implementation of this load shifting technique was carried out on three different types of loads, these being residential loads, commercial loads, and industrial loads. Two case studies, over summer and winter, were validated to check the feasibility of the test system. The proposed method aimed to achieve the load demand in an effective way for the minimization of bill electrification, Peak to Average Ratio (PAR), and the consumption of power. The Time-of-Use (TOU) pricing was implemented to calculate the savings in energy bills. The proposed test system of the Micro Grid (MG) was executed on a MATLAB platform with two case studies based on the optimization methods WFSA and WFS2ACSO. Simulation results demonstrated the comparative analysis of electricity cost and peak load with different algorithms and were carried out with and without DSM consideration. The projected DSM methodology achieved considerable savings as the peak load demand of MG decreased. Furthermore, the decrease in PAR levels of 14% in the residential load, 16% in the commercial load, and 10% in the industrial load, with and without the DSM methodology, was presented. The flight length and awareness of probability tuning parameters make the proposed algorithm more effective in obtaining better results. The test results obtained prove the effectiveness of the hybridized algorithm as compared with other trend-setting optimization techniques such as Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO).

Electricity demand in populations gaining access: Impact of rurality and climatic conditions, and implications for microgrid design

Almost 800 million people currently lack access to reliable electricity, for many of whom solar microgrid systems are expected to be the most cost-effective solution. Quantifying current and future electricity demand is crucial for cost-effective design of reliable microgrids. However, electricity usage is connected to a wide range of social and economic factors alongside climatic conditions, making estimation of demand challenging. This paper presents a framework facilitating each stage of solar microgrid design from demand estimation through to cost-optimal sizing of the microgrid and its economic and environmental characterisation. Household demand is simulated based upon (1) climatic conditions, (2) appliance ratings and usage patterns, and (3) rates of growth in appliance ownership based upon the Multi-Tier Framework for measuring household electricity access. Microgrid demands are simulated based on the combination of these with (4) nondomestic demand based upon locally available data. The framework is demonstrated across four rates of domestic demand growth and two climatic conditions (‘tropical savanna’ and ‘humid subtropical’), alongside nondomestic demand based upon two operational microgrids (one rural and one peri-urban). When growth rates are high, newly introduced appliances tend to dominate, with differing impacts on the demand profile depending on power and usage times. Cooling represents a modest contribution to demand in the tropical savanna climate. However, in the hotter and more seasonally varying humid subtropical climate, cooling becomes the dominant driver of demand, increasing seasonality and proportion of demand at night. Nondomestic demand in the rural microgrid is primarily agricultural, and exhibits more seasonality and better alignment with daylight hours than demand in the peri-urban setting, which is more service-based. Across cases, increased seasonality and proportion of demand at night lead to poorer alignment with PV generation, increasing cost and GHG emissions per unit of electricity used in a cost-optimised microgrid system.

Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components

Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush–Kuhn–Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is verified through numerical simulations and discussion of their results.

Optimal scheduling of distributed energy resources in microgrid systems based on electricity market pricing strategies by a novel hybrid optimization technique

The upsurge in microgrid demand is an important aspect of imparting energy in future primarily because of the involvement of renewable energy sources, which alleviates the emission of toxic gases from fossil fuelled generators. The grid-connected mode of microgrid operation is the most economical and definitive mode of service wherein the grid is actively involved in the buying and selling of power prompting diminished generation cost of microgrid system. These cases, pertaining to two different low voltage microgrid systems, are applied consecutively for obtaining the generation cost of the systems and thus devise the cheapest strategy among them. The Grey Wolf Optimizer (GWO) is improvised by incorporating strategies from population-based Sine Cosine Algorithm (SCA) along with position updating methods of crows from Crow Search Algorithm (CSA) to form a hybrid modified Grey Wolf Optimizer Sine Cosine Algorithm Crow Search Algorithm (GWOSCACSA) algorithm. The implementation of the proposed technique produces a comprehensive generator cost reduction of the microgrid system. It was evident from the results that generation cost was minimum when Time of Usage (TOU) based market pricing strategy was considered. Further, it was also established that dynamic grid participation was reduced 47% in the system generation cost for the same scenario compared to the case when the grid was operating passively. The statistical analysis endorses the improvements of GWOSCACSA over other algorithms presented in the state-of-art-literature.

Seamless transition of microgrid between islanded and grid‐connected mode of operation

Microgrids and their smart interconnection with utility are the major trends of development in the present power system scenario. Inheriting the capability to operate in grid-connected and islanded mode, the microgrid demands a well-structured protectional strategy as well as a controlled switching between the modes. This challenging task is dealt with in this study, by the proposed centralized smart mode transition controller (CSMTC). The controller embarks upon two major microgrid protection aspects, by incorporating the protectional strategy against unintentional islanding and auto-reclosing. Subsequent to the protection of the microgrid, the smooth operation of the microgrid has also been a major focus of the proposed study. Therefore, the switching of microgrids between the modes (i.e. grid-connected to islanded or vice-versa) has been engaged in the proposed controller. Energy storage-based distributed static synchronous compensator (E-STATCOM) is integrated at the point of common coupling to support the performance of the controller. E-STATCOM performs to compensate the switching transients, along with maintaining the steady-state system stability. The CSMTC integrated with E-STATCOM protects the microgrid against unwanted system faults and supports a seamless transition between the modes by controlling the interconnecting static switch. To verify the operation of the proposed control strategy, the microgrid test model is simulated on the podium of MATLAB 2015b/Simulink.

A novel approach for sizing thermal and electrical energy storage systems for energy management of islanded residential microgrid

This paper proposes an energy management scheme (EMS) for islanded operation of a residential Microgrid (MG) using a Lithium-Ion battery (LIB) energy storage system (LIBESS) and distributed generators (DGs). The LIBESS handles primary frequency control and energy management during peak-load period while the dispatchable DGs supply the base load. During peak-load periods, the thermostatically controlled loads (TCLs) like air conditioners (ACs) make up a significant part of MG demand. A Thermal energy storages (TES) is quite similar to a TCL except that it can also store thermal energy for later use. Here, instead of using the ACs, it is proposed to use a TESS to provide the required thermal energy for keeping the inside temperatures of residences within the desired range. Instead of using the ACs, a pre-charged TES can be used to provide the needed cooling thermal energy during islanded operation that power generation capacity is insufficient. By not using the ACs, the total energy consumption of MG and the required LIB capacity for energy management decreases considerably. Various daily scenarios from available recorded data and randomly generated data are used to consider the uncertainties of the variations of the MG load profile, the ambient temperature and the renewable power generations. Also, the outage of the DG with the largest generation capacity is considered as the worst contingency that could ever happen during the islanded operation of MG. The LIBESS sizing results with and without the outage of the DG show that with the ACs being totally replaced with a TES, the overall cost of the TES and the LIBESS is decreased by 33% and 63% respectively than the case that the TES is not utilized.

Comprehensive electro‐thermal battery‐model for Li‐ion batteries in microgrid applications

AbstractBattery energy storage systems (BESSs) are frequently used to buffer the difference between intermittent renewable generations and energy demand in microgrids. The operation of BESS is affected by dynamics of charging/discharging current, internal temperature build up, extreme reaches of state of charge level, and so forth. This article presents a comprehensive electro‐thermal model for Li‐ion batteries that can be used to investigate dynamic and static performances of a microgrid under real time operating conditions. The battery‐model has the ability to self‐update its parameters with the variation of core‐temperature, and also to accommodate inherent hysteresis present on parameters between charging and discharging events. The developed model is presented as a block in MATLAB/Simulink for easier use by others. In parallel with that, the details of the development of a complete simulation platform of a microgrid is also described, which includes battery charging and discharging converter systems, bidirectional grid‐end AC/DC converter system, wind energy input, solar PV energy input, load and closed loop control associated with converter systems. The battery model is simulated within the microgrid platform with a chosen energy management criteria. The results of the simulation are also presented and discussed.

An optimized Energy Cost Analysis using Fuzzy Logic Control Algorithm for the autonomous Microgrid

In this paper, the significance of Fuzzy Logic Control Algorithm is proposed for determining the effective cost analysis of power generation with respective to the load demand in microgrids. There are many conventional algorithms are developed in the past to evaluate the cost analysis with system constraints, however, they are complex and time consumed. Also, these algorithms are mainly used for conventional systems and are not suitable for microgrids. To overcome these disadvantages, a simple Fuzzy Logic Control Algorithm is used to define the cost analysis effectively. The features of these algorithms are eaisly adapted and incorporated with the objectives of Demand Side Management stratigies in microgrids. In addition, the correlation and an Artificial Bee Colony Optimization technique is proposed between the variables of PV and Wind turbine to obtain the highly accurate power generation samples with respect to the load demands. The analysis of optimization and Fuzzy algorithms are developed using MATLAB Simulation software and the cost analysis are shown for different load variations.

Research on Multi-Microgrids Scheduling Strategy Considering Dynamic Electricity Price Based on Blockchain

In order to improve the economy and safety of multi-microgrids (MMGs) scheduling, this paper proposes a research on MMGs scheduling strategy that takes into account dynamic electricity prices based on the blockchain. We first introduce the principle of blockchain, analyze the security and economy of the combination of blockchain and MMGs scheduling, and design the scheduling architecture and process based on the blockchain platform. Second, we set a dynamic electricity price model according to the total power supply and demand of MMGs, and set a load optimization model. Finally, we take optimal system economy and minimum environmental pollution as the objective function, then use the linear programming method and the improved krill herd algorithm (KHA) with nonlinear changes in weights to solve the problem. The simulation results show that: (1) The dynamic electricity prices can reflect the power supply and demand of microgrids and optimize the load; (2) Comparing the three scheduling schemes, the strategy in this paper can improve the economic and environmental protection of MMGs by 37.33% and 39.34%, while reduce the interactive power between the microgrid and the distribution network by 56.28%, and the curtailment rate by 63.22%; (3) The improved krill herd algorithm has higher convergence speed and convergence accuracy;(4) The blockchain technology can ensure the security of scheduling data.

Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty

Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy the demand of another MG or a group of MGs that form an MG community. These communities can be dynamically established through time, based on the variations of demand and supply of the interconnected MGs. In many modern MGs, Electric Vehicles (EVs) have been considered as a viable storage option due to their ease of use (plug-and-play) and their growing adoption rates by drivers. On the other hand, the dynamic nature of EVs escalates the uncertainty in the transactive distribution system. In this paper, we study the problem of energy trading among MGs and EVs with the aim of power loss minimization where there is uncertainty. We propose a novel Bayesian Coalition Game (BCG) based algorithm, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently. The proposed scheme is compared with a conventional coalitional game theory-based approach and a Q-learning based approach. Our results show significant improvement over other compared techniques.

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Nowadays, distributed energy sources are widely used to supply demand in micro grids (MGs) especially in the green buildings. Despite increasing number of intermittent distributed energy resources, MGs have essential influence on decreasing use of conventional generation, but it give rise to new challenges in terms of energy management, stability and reliability of system. Multi-agent systems (MASs) as distributed smart units have been widely used recently. However, control and implementation of these smart structures enhance existing challenges because these units need a framework that, at first, guarantees operation of MASs and then insures management in generation and demand side. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. An energy management system (EMS) based on multi-agent system (MAS) controller is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting reliability characteristic. In the proposed approach, a self-healing hierarchical algorithm is implemented to control interaction of agents and also guarantees reliability of smart control system under faults. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a robust and stable control of a microgrid.

Planning Framework for Optimal Resource Utilization Strategy in Microgrid

The incorporation of distributed energy resources and technical innovations has restructured the electrical power system network from conventional grid to microgrid. Consequently, significant impacts on both distribution and utilization of power have been observed triggered by diverse operating condition and enhanced power demand. In planning of microgrid, energy management is a critical issue which is related to efficient utilization of available resources at minimum operating cost. Distributed and intermittent nature of generations in microgrid demands efficient and optimal allocation of existing resources. An allocation technique is necessary to attain optimal utilization of assets and to accomplish decision making for optimal power flow route through shortest path. This paper proposes a multi-objective optimization-based resource allocation technique to efficiently control available generation at any load condition to achieve minimum operating cost while maintaining all system constraints. In proposed framework, line flows are maximized to its load-carrying capability and by controlling power flow through shortest possible path. Proposed methodology suggests proper controller position to sustain desired power flow through the shortest path between each generator and load pair. An interconnected microgrid network topology is considered to demonstrate the proposed methodology.

Application Conditions of Bounded Rationality and a Microgrid Energy Management Control Strategy Combining Real-Time Power Price and Demand-Side Response

Microgrid energy management is a typical discrete non-linear optimization problem that is usually solved by off-line optimization, day-ahead demand-side management, and long-term dynamic optimization scheduling strategy. However, due to the intermittent distributed generation and time-varying load in microgrids, more attention should be paid to the real-time optimal scheduling of the overall operation of energy to ensure the dynamic balance of supply and demand in microgrids. Combining demand-side response with real-time power price, this paper applies the strategy to microgrid energy management and proposes a distributed energy real-time management model of microgrid based on demand-side response function. A deep adaptive dynamic programming optimization algorithm is also proposed for the model. The real-time interaction between microgrid operators and users is realized. The closed-loop feedback control structure of the proposed model ensures the real-time optimization control strategy. Therefore, the proposed energy management model and control strategy can realize intra-day dispatching in microgrids. The real-time performance and effectiveness of the proposed energy management model and control strategy are also verified by numerical simulation. Finally, since the proposed model is approximate, whether the solution obtained by the algorithm is the optimal or satisfactory solution of the optimization strategy set is a lack of theoretical support. Therefore, according to the approximation theorem of bounded rationality, the application conditions of the model in power markets are proposed. It is proved that the proposed model meets the application conditions, and is a specific application of bounded rationality approaching complete rationality in the power market. It is also proved that the best solution is involved in the satisfactory solution set of the model. Thus, the control strategy is a rational and feasible optimal management control strategy, which provides a theoretical basis for its further implementation.

Optimal Operation Scheduling of a Microgrid Incorporating Battery Swapping Stations

This paper proposes optimal operation scheduling of a Microgrid (MG) and battery swapping stations (BSSs) as two independent stakeholders with inherently conflicting objectives. In this regard, a bi-level scheduling framework for optimal decision making of MG and BSSs is presented. Moreover, battery degradation cost is explicitly modeled based on the depth of discharge and the cycle life's intrinsic behavior of batteries. In order to tackle both historical data-based and human-related uncertainties under incomplete information including load demand of MG, photovoltaic generation, wholesale market prices, and swapping requests, a hybrid probabilistic-possibilistic approach considering correlation among uncertainties has been developed. To solve the proposed MG-BSS optimization problem, alternative direction method of multipliers (ADMM) with restart algorithm in a fully decentralized fashion is implemented. The effectiveness of the proposed model is demonstrated on a real-test MG system under different scenarios. Moreover, to compare the computational complexity of the proposed algorithm with the standard ADMM and investigate the scalability of the algorithm, extensive simulations are carried out on different standard test system.

Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components

Operation scheduling is one of the most practical optimization problems to efficiently manage the electric power supply and demand in microgrids. Although various microgrid-related techniques have been developed, there has been no established solution to the problem until now. This is because the formulated problem becomes a complicated mixed-integer programming problem having multiple optimization variables. The authors present a framework for this problem and its effective solution to obtain an operation schedule of the microgrid components considering their coordination. In the framework, trading electricity with traditional main power grids is included in the optimization target, and uncertainty originating from variable renewable energy sources is considered. In the solution, the formulated problem is reformulated to reduce the dimensions of its solution space, and, as a result, a combined algorithm of binary particle swarm optimization and quadratic programming is applicable. Through numerical simulations and discussions of their results, the validity of the authors’ proposal is verified.

Spatio-temporal information based protection scheme for PV integrated microgrid under solar irradiance intermittency using deep convolutional neural network

PV integrated microgrids are characterized by increased intermittency because of the uncertainty in solar irradiance level. In an islanded mode, the irradiance variation significantly affects the current profile both during the healthy and faulty scenario, thereby increasing the risk of maloperation of conventional relays based on threshold settings. A fault-resilient and reliable microgrid demands the development of a protection scheme, which is robust to varying irradiance levels. In this regard, this paper proposes a protection scheme based on Convolutional neural network (ConvNet) approach of deep learning for fault detection/classification, section identification and location during islanded mode. The use of ConvNet allows identifying discriminatory attributes from complex datasets with reduced computational cost. The time-domain voltage and current signals retrieved from the relaying bus post-fault are fed as input to the respective ConvNets trained to perform the intended tasks of classification and regression. The uncertainty in irradiance has been incorporated by modeling irradiance levels over a period using probability distribution function (PDF). The incorporation of spatio-temporal data-based distribution function within the framework of spatial data based ConvNet allows for improved mapping between the multi-domain data and state of the microgrid. The performance of the proposed technique under varying irradiance and fault scenarios has been analyzed through statistical indices and Monte-Carlo simulations. Further, the appropriateness of proposed technique has also been validated for real-time setting using the OPAL-RT digital simulator.

PSO Based Dispatch and Control of Hybrid Power System with PV-Diesel Generator- A Prototype

A micro-grid is a small-scale power grid that can operate independently or in conjunction with the area’s main electrical grid. Micro-grid paves way to power remote locations and effectively integrate various sources of distributed generation, including renewable. In this paper, an economical way of dispatching PV power and power generated by generators in islanded micro-grid is proposed. Here, part of the micro-grid demand is supplied by PV power station. The demand which cannot be supplied by the PV panel, are dispatched between DGs by solving unit commitment and economic dispatch problems. Unit commitment is carried out using priority list method and economic dispatch particle Swarm optimization. A hardware prototype for the proposed micro-grid dispatch methodology has been developed. Here, commitment and de­commitment of the sources and demands is carried out by generating control signals denoting the ON/OFF status to the sources for different intervals.

Optimal performance of microgrid in the presence of demand response exchange: A stochastic multi-objective model

This paper investigates optimal scheduling of the microgrids, including renewable energy sources and conventional generators. Due to the stochastic nature of the wind speed and the sun irradiation, generated power of the wind turbines and photovoltaic are highly uncertain and in the proposed model the uncertainty of the load, price, and renewable energy sources generation are considered by utilizing the normal distribution function. Incentive-based demand response program is implemented in the operating process. The optimal economic status is achieved by maximizing the microgrid's demand response program profit and minimizing the generators cost, and the trading cost. This multi-objective model is solved by the weighted sum technique in order to produce the optimal Pareto solutions and the trade-off solution is selected by applying the fuzzy satisfying method. It is performed on two different microgrids and sensitivity analysis is executed. Results demonstrate that demand response program reduces unused energy in both scenarios.

Development of dynamic phasor based higher index model for performance enhancement of dual active bridge

Renewable energy integration and control in microgrid demands computationaly efficient model along with effective control methodology for switched converters. The dual active bridge (DAB) being an integral part of the microgrid suffers from the stability issues. This research proposes dynamic phasor (DP) based higher index model of DAB and demonstrates the effect of appropriate selection of model on performance. To evaluate the system stability for performance objectives of voltage regulation, suppression of measurement noise, sinusoidal disturbances and robustness, a robust control strategy based on loop shaping technique for DP model of DAB is proposed. Comparison of the responses of the higher index model with existing DP model with dc and index 1 terms makes apparent the benefits of the proposed model. The Hardware-in-Loop (HIL) simulation using Opal-RT and dSPACE simulators is carried out for validation of the proposed control scheme.

Enhanced structure and optimal capacity sizing method for turbo-expander based microgrid with simultaneous recovery of cooling and electrical energy

In this paper, a novel configuration of microgrid with incorporation of turbo-expander (TE) has been presented. The cooling and electrical energy recovered by TE are used to supply the microgrid demand. The purification and preheating processes are imbedded on the inlet gas, which, according to the need, provide and control the recovery of electrical energy and cooling. Due to the unique features and limitations of TE, determination of type and optimal size of energy resources in this microgrid is special; so an innovative optimal capacity sizing (OCS) method was presented. For determination of optimal size of TE, the constraints and needs of the local natural gas (NG) network should also be considered, which, differentiates it from other energy sources. Another key feature of the proposed OCS method is optimization of inlet NG temperature passing through TE.Performance and economic evaluation of the TE-based microgrid was compared with the traditional microgrid without TE. Analysis showed that O&M and emission costs of the TE-based microgrid were reduced significantly (about 27% and 30%, respectively) culminating in low prices for the generated energies. In addition, net present value and internal rate of return of the TE-based microgrid were higher (38.5% and 6%, respectively).