Scheduling Of Microgrid Research Articles

Frequency Stability Constrained Microgrid Scheduling Considering Seamless Islanding

The frequency stability of microgrids is of paramount importance due to their low inertia and high share of renewable energy sources. In this paper, a frequency stability-constrained microgrid scheduling (FSC-MS) model is developed based on a time-dependent discretized frequency response model considering seamless unintentional islanding events. The optimal dispatch of controllable units, as well as optimal upward and downward primary reserves, are determined with a minimum operating cost while ensuring frequency stability criteria are maintained in their safe ranges following an unintentional islanding event, such as rate of change of frequency (RoCoF) and frequency nadir and overshoot. The limitation of generators with respect to primary frequency response is formulated in the form of primary reserve capacity and primary ramp rate limits. A cost-based model is presented to describe the available primary reserve of synchronous generators. The proposed FSC-MS model is formulated as a mixed-integer linear programming (MILP) problem using Benders decomposition. Simulation results on a medium voltage microgrid test system demonstrate the effectiveness of the proposed model to meet the frequency stability constraints while minimizing the total operating cost.

Robust Microgrid Scheduling Considering Unintentional Islanding Conditions

This work presents a novel microgrid scheduling model considering the stochastic unintentional islanding conditions as well as forecast errors of both renewable generation and loads. By optimizing the dispatch of distributed energy resources (DERs), utility grid, and demand, the proposed model is targeted to minimize total operating cost of the microgrid, including start-up and shut-down cost of distributed generators (DGs), operation and maintenance (O&M) cost of DGs, cost of buying/selling power from/to utility grid, degradation cost of energy storage systems (ESSs) and cost associated with load shedding. To capture the stochastic unintentional islanding conditions and conventional forecast errors of renewable generation and loads, a two-stage adaptive robust optimization is proposed to optimize the objective function in the worst case scenario of the modeled uncertainties. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The result obtained ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. The proposed model is validated with results of case studies on a microgrid consisting of various DGs and ESSs.

Combined compromise solution and blockchain-based structure for optimal scheduling of renewable-based microgrids: Stochastic information approach

In this paper, a novel secured architecture is developed based on the blockchain technology for the energy management program (EMP) of renewable-based microgrids. Indeed, the blockchain technology has been used for securing the data transaction among the agents, e.g., generators, consumption points, and the central control unit of the microgrid according to the hash function concept. To make the proposed framework more realistic, a stochastic framework based on fuzzy cloud theory is developed to capture the uncertainty effects from the renewable energy sources, as well as market electricity price and the hourly load demand. Each drop in the fuzzy cloud recognizes a deterministic framework for the EMP with a specific probability value. The EMP in a renewable microgrid with different types of power sources such as wind turbine (WT), photovoltaics (PV), storage, fuel cell (FC), and microturbine (MT) in a stochastic environment is a challenging task that requires a global solver for getting to the optimal operating point. To this end, a smart optimization method based on the θ-firefly algorithm (θ-FA) is designed which would look for the optimal solution in the polar space rather than the traditional Cartesian space. In addition, an adaptive modification approach based on local and global search operators is devised which can help the algorithm to make a mature optimization process. Two different scenarios are simulated in the paper to assess the different aspects of the proposed architecture in typical renewable microgrids incorporating the WT, PV, FC, MT, and storage.

Energy Scheduling Framework of Micro-Grids Considering Battery Lifetime

The stochastic and intermittent nature of renewable energy poses grave challenges to the energy scheduling of micro-grids. The exploitation of batteries has been considered as an effective way to cope with such challenges. However, fluctuation in renewable process causes batteries to charge/discharge frequently, reducing the lifetime and increasing the maintenance cost. This paper presents a hierarchical two-layer energy management, a combination of battery and supercapacitor, to lessen daily energy costs. The upper layer is equipped with a battery to minimize the economic cost, optimizing the energy usage by employing a mixed-integer nonlinear programming (MINLP) model; the lower layer is equipped with a supercapacitor to treat the uncertain nature of renewable energy. The optimization process includes load shifting and battery degradation costs. The upper layer determines the optimal schedule of interruptible appliances and the profile for energy storage for the next 24 hours. The schedule results are then passed to the lower layer, which deals with the forecast uncertainties with a supercapacitor with a 15 minutes interval. The effectiveness of the proposed algorithm is examined by a single-layer scheduling system without forecast errors and a two-layer scheduling system with forecast errors. The obtained results show the capability and effectiveness of the proposed scheduling system to reduce operating costs and forecast errors.

Deep reinforcement learning based bi-layer optimal scheduling for microgrid considering flexible load control

Deep reinforcement learning based bi-layer optimal scheduling for microgrid considering flexible load control

Resilient Microgrid Scheduling With Secure Frequency and Voltage Transient Response

The resilient operation of microgrids (MGs) relies strongly on their ability to operate in islanded mode, autonomously from the bulk grid, whilst adhering to secure operation requirements. Catastrophic events in the transmission grid can lead to abrupt MG islanding accommodated by large frequency and voltage excursions due to power imbalances within the MG. It is vital that MG scheduling algorithms incorporate both static and transient security metrics to ensure a secure transition during islanding, immunised against the transient phenomena. In this paper, we incorporate both frequency- and voltage-related security constraints in a MG operational planning problem to ensure robust operation against abrupt islanding events. We employ an iterative dynamic optimization approach, based on the sensitivities of active and reactive power injections to the system security metrics, to incorporate the transient and static security constraints in the planning problem. Due to their non-linear and intractable nature, the transient security constraints are reformulated as linear sequential resilience cuts resulting in a computationally efficient problem. The performance of the algorithm is shown on a 30-bus, 20 kV, distribution network, subject to a 24-hour variation in load and renewable generation.

Microgrids and Resilience: A Review

The occurrence of large-scale disturbances is increasing at an alarming rate throughout the world. As a consequence of this trend, a primary concern of today’s power system is to enhance its resilience against low-probability, high-impact events. In this regard, microgrids, as the smart grid’s building blocks, offer promising approaches toward achieving higher levels of distribution system resilience by accommodating and integrating various distributed energy resources. Accordingly, microgrid-based techniques have been the focus of a growing body of research seeking a more resilient power system. These methods mainly rely on the stand-alone operation of microgrids to supply loads locally in case of extreme events. The objective of this paper is to present an updated comprehensive review of the literature on two main categories of microgrid-based resilience enhancement approaches in distribution systems: 1) optimal microgrid formation and 2) optimal microgrid scheduling and energy management. Distinctive from other review papers, this article systematically surveys the research studies under multiple well-sectionalized features, such as various technologies, techniques, models, constraints, and concepts for each of the above two categories. These features include but are not limited to networked microgrids, demand response programs and electric vehicles scheduling, multi-energy microgrids, dynamic optimization schemes, control schemes, communication resilience, hybrid microgrids, and mobile energy resources. Additionally, a comprehensive introduction to resilience definitions and assessment methods, microgrid components, architectures, and control schemes, as well as, sources of uncertainty is provided.

Assessing the Value of Proactive Microgrid Scheduling

Assessing the Value of Proactive Microgrid Scheduling

Multimode Coordination Control Method for Microgrid Cluster Based on Adaptive Power Control and Routing Algorithm

Aiming at the coordinated control problem caused by distributed energy belonging to different agents, a multimode coordinated control method of microgrid group based on adaptive power control and routing algorithm is studied. According to the characteristics of microgrid group, a method combining adaptive power control with routing algorithm is proposed to predict the channel interference of nodes and select the next hop of nodes. The nodes adjust their power to adapt to their own surrounding environment to ensure the maximum throughput of microgrid under the minimum channel interference; on this basis, a multi-agent system is introduced to establish the energy’s intelligent coordinated control strategy of multi microgrid, a three-layer coordinated control system framework is designed, and the solution process of regional autonomy and global collaborative optimal control is proposed. The results show that the proposed multimode coordination control strategy can be used to optimize the multi microgrid scheduling in grid connected and islanded modes. The energy storage system can obtain better economic benefits in the economic optimal mode, while the longest life mode can reduce the economic benefits in order to ensure the smooth operation of the energy storage system.

A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm

In the stochastic optimal scheduling of microgrid with multiple wind farms, the accurate description of uncertainties is a critical issue. Scenario generation provides an effective way to represent the strong randomness and interdependence between wind speeds. However, there may be very limited data or no historical information in the beginning stage of a newly-built wind farm operation, which will lead to the inaccuracy of scenario generation and thus affect the reliability of decision results. In this paper, considering that multiple wind farms in the adjacent areas may have similar weather conditions, a novel transfer learning-based scenario generation method is proposed to utilize the historical information from other existing data-rich farms for generating wind speed scenarios of the new farm. The scenario generation tasks are constructed as a cross-domain adaption problem. To model the target wind speed, joint distribution adaption (JDA) is adopted to explore the underlying relationship between multiple source farms and the target farm. Experimental results show that the scenarios generated by our proposed method can better describe the properties of target wind speed, and the microgrid scheduling results can be more reliable in the case of very limited data.

Dominated GSO algorithm for optimal scheduling of renewable microgrids with penetration of electric vehicles and energy storages considering DRP

With the growth of distributed energy resources, the development of storage systems technology and an increase in the application of electric vehicles (EV), the energy management of these kinds of sources needs additional research. The task of the distribution network operator is to optimise the microgrid operation at the lowest operating cost. This paper suggests a novel concept for parallel scheduling of EVs and battery storage while considering demand response programme (DRP) to minimise total operating costs. The influence of cooperation of DRP and optimal scheduling of EVs and energy storages on the operating costs, power transaction with upstream grid, hourly DERs, and systems’ technical features, such as voltage profile and power loss, have been studied. Numerical results obtained from an illustrative case study elucidate that the proposed model reduces 5.62 per cent from total power loss and improves the minimum voltage from 0.91 to 0.94 p.u.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%.

Energy management system using Mimosa Pudica optimization technique for microgrid applications

A microgrid is an powerful alternative promising solution, which fosters a reliable power supply to supplement conventional electrical framework. However, microgrid encounters various challenges subjected to energy flow, power quality, and net profit due to the high integration of distributed energy resources (DERs) in the network. Hence, the Mimosa pudica-based energy management scheme has been designed for optimal scheduling of microgrid to reduce the production overheads with underlying system constraints. Besides, the proposed approach is formulated to manage the power balancing among the distributed resources and utility through a standard communication protocol. Mainly, the optimization process is developed through the sensitive and intelligent behavior of mimosa plants that enable the adaptation of dynamic context by reusing past information through memory, and maintaining variation across the solution leads to good solution accuracy. Moreover, the proposed algorithm corroborates the potency of microgrid over isolated, grid-tied, and resynchronized conditions. Numerical results illustrate that the proposed technique has obtained more profit (8%) than the existing approaches with superior optimization ability and fast convergence.

Frequency-Constrained Resilient Scheduling of Microgrid: A Distributionally Robust Approach

In order to prevent the potential frequency instability due to the high Power Electronics (PE) penetration under an unintentional islanding event, this paper presents a novel microgrid scheduling approach which includes the system frequency dynamics as well as the uncertainty associated with renewable energy resources and load. Synthetic Inertia (SI) control is applied to regulating the active power output of the Inverter-Based Generators (IBGs) to support the post-islanding frequency evaluation. The uncertainty associated with the noncritical load shedding is explicitly modeled based on the distributionally robust formulation to ensure resilient operation during islanding events. The resulted frequency constraints are derived analytically and reformulated into Second-Order Cone (SOC) form, which are further incorporated into the microgrid scheduling model, enabling optimal frequency services provision from the micorgrid perspective. With the SOC relaxation of the AC power flow constraints, the overall problem is constructed as a mixed-integer SOC Programming (MISOCP). The effectiveness of the proposed model is demonstrated based on modified IEEE 14-bus system.

Multi-objective economic optimization scheduling of CCHP micro-grid based on improved bee colony algorithm considering the selection of hybrid energy storage system

By factoring into the model selection of a hybrid energy storage system, this paper established an economically optimized multi-objective dispatching model for a micro-grid of Combined Cooling Heating and Power (CCHP) based on an improved Algorithm of Artificial Bee Colony (ABC). To upgrade the single-objective model in previous studies, the optimization goal of the model is to minimize the daily power generation dispatching cost and daily environmental pollutant treatment cost of the micro-grid, and the improved Algorithm of ABC based on Beetle Antennae Search Algorithm (BAS-ABC) was used to obtain a solution. To achieve a better application of theoretical results, the paper studied the summer typical daily real power load data of a grid-connected CCHP micro-grid in a district of Shanghai, and simulated a multi-objective economical dispatching model under the condition of using the Time of Use (TOU) billing system. The result shows that the minimum cost derived from the solution of the optimized ABC is lower than that from the traditional ABC. Furthermore, when the objective function of the CCHP microgrid is the lowest power generation cost or the lowest environmental cost, the optimized results are contradictory: it is impossible to simultaneously achieve an optimal power generation cost and an optimal environmental cost. This indicates the multi-objective optimized dispatching approach has comprehensively balanced the economic efficiency and the environmental-friendliness of the system.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

A two-stage hybrid robust-stochastic day-ahead scheduling of transactive microgrids considering the possibility of main grid disconnection

The interconnected transactive MGs (ITMGs) concept is an efficient way for economical operation of MGs. For power systems under the danger of stochastic disasters, a priority should be given to the resiliency enhancement of these ITMGs to have the highest restoration in the times of disconnection from the main grid. This paper introduces a two-stage hybrid stochastic programming/robust optimization (SP/RO) day-ahead scheduling of ITMGs that takes into account the possibility of disconnection from the main grid for several hours. The proposed resiliency-oriented scheduling in this paper is a two-stage mixed-integer linear optimization problem and aims to reduce the operation cost of the whole ITMG. Furthermore, the excess power can be traded among MGs apart from the available power of renewable resources, dispatched power of storage and dispatchable units, and demand response programs. Thus, this scheduling needs to consider the optimal hourly transactive energy among the MGs in both normal and grid disconnected modes of the ITMG. The load curtailment and demand response programs are the last options for maintaining the balance in the conventional structure of MGs that this paper utilizes for ITMG. Obtained results showed that load curtailment of the ITMG is limited by the implementation of transactive energy between MGs. Furthermore, the reduction in renewable and dispatchable generation capacity and their impact on transactive energy and demand response programs during resiliency is discussed. Also, to enhance the conservativeness and to consider the worst cases in this scheduling, the uncertainties in the start time of the islanding and its duration are considered in the problem.

Security‐constrained optimal scheduling and operation of island microgrids considering demand response and electric vehicles

As an independent unit, microgrid operator (MGO) should respond to the energy demand of customers with an optimal provision cost. Apart from the ability to guarantee the security of microgrid (MG) operation, the MGO should consider the greenhouse emission effects as well. This paper investigates a novel energy management system for an islanded MG. In the proposed strategy, two efficient methodologies are used to maximize the profit and security of the MG: (a) the optimal operation of electric vehicles (EVs) in vehicle-to-grid (V2G) mode and (b) the use of demand response (DR) program. Besides, a hierarchical control structure is proposed to manage frequency and voltage uncertainties in a permissible range. Improving the reliability of the MG is another goal of the proposed strategy, which is pursued by the studied controlling framework. The proposed approach is evaluated through a set of simulation and GAMS tests. Results indicate that the frequency deviation of the MG is damped through the proposed management strategy and place the spotlight on the effectiveness of the approach.

Consumer-centric and capital efficient design of community microgrids for financially-Strapped communities

ABSTRACT Subscription-based microgrids have been deployed to serve electricity needs of rural communities due to their simple user interfaces and basic energy management requirements. However, these projects often suffer from economic inefficiency to sustain long-term operations, lacking insufficient price signals and demand side, as well as uncertainties around consumer participation. This article proposes a consumer-centric planning and design framework for community microgrids, based on capacity subscription with self-rationing to reveal consumers’ real preferences in service reliability and their budget constraints, in order to achieve social welfare maximization and revenue adequacy. This capital efficient community microgrid greatly simplifies the microgrid investment planning, operation and scheduling, and reduces capital requirements and transaction costs, thus to improve electricity accessibility and affordability and to incentivize private sector investments. This is particularly advantageous in microgrid applications for financially strapped communities when incorporating high share of variable renewable energy generations. Requiring a lower level of investments than the current subscription-based microgrids, our analysis shows the benefits of the proposed design in improving the consumers’ surplus, and supply–demand matching, achieving cost recovery, and desired level of service reliability for consumers.

Optimal day ahead energy consumption management in grid‐connected microgrids

The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on input-output characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strategy is adopted to reduce the peak loads and enhance the DG's unit operational costs. The impact of demand-side management and price-driven demand response programs on convex and nonconvex energy management system (EMS) problems is investigated. Furthermore, the short-term scheduling horizon of 15-minutes resolution time is considered for both solar and wind power to maintain forecast accuracy. The state-of-art optimization algorithm of quantum particle swarm optimization is devised to solve the proposed problem in the presence of the nonconvex cost function of DGs. The technical performance indices for each demand response program is evaluated, and the best alternative demand response program is chosen by implementing analytical hierarchy process. The proposed algorithm efficiently solves the nonconvex EMS problem, and the simulation results yield a 12.11% reduction in operating cost without compromising customer satisfaction. Computational time, convergence characteristics, and solution effectiveness in contrast to recently reported metaheuristic algorithms are examined for the effectiveness of the suggested algorithm.