Operation Of Microgrid Research Articles

Locational marginal price based scheduling strategy for effective utilization of battery energy storage in PV integrated distribution system

Integration of renewable energy resources (RERs) and complexities associated with varying load patterns urge the demand for energy storage systems (ESS) to ensure real power balancing of distribution system. Among various ESS, battery energy storage system (BESS) guarantees the reliable operation of microgrids in terms of economic and technical functionalities. The scheduling of microgrids becomes essential for the economic and optimal performance of both generating sources and loads. In this work, the load-shifting capability of BESS is utilized for the optimal scheduling of grid-connected microgrids. BESS has a significant role in the effective utilization of power from RERs during grid balancing. This paper discusses the scheduling of grid-connected microgrid with two scheduling strategies, which differ in the charging behaviour of BESS. Scheduling decisions are based on the value of locational marginal price (LMP) of distribution system. In scheduling strategy-I, BESS charges at off-peak hours, either from the PV system or from the grid. Meanwhile, in scheduling strategy- II, BESS charges from the PV system at peak or off-peak hours. These two scheduling methods are also compared with the scheduling of the distribution system without integrating the microgrid. The cost of power generation, power from the main grid, and active power losses of the distribution system are calculated by performing optimal power flow (OPF). The effectiveness of the proposed scheduling strategies are validated on the IEEE-69 bus system for 24 h time duration by using MATPOWER simulation tool. The results obtained after OPF establishes the requirement of effective utilization of BESS in PV integrated distribution system.

Mechanism Analysis of the Effect of the Equivalent Proportional Coefficient of Inertia Control for a Doubly Fed Wind Generator on Frequency Stability in Extreme Environments

With the large-scale access of a doubly fed wind generator (DFWG) with inertia adjustment capability to the polar microgrid, the frequency stability characteristics of the polar microgrid become more complicated. To enhance DWFG frequency stability and ensure the safe and reliable operation of polar microgrids, a DFWG connected to a two-region interconnected polar microgrid is used as the research background for this paper. Firstly, the equivalent model of two regional inertia centers is derived, and the effect of DFWG virtual inertia on the rotor motion equation of the regional inertia center is analyzed when a DFWG is directly connected to the polar microgrid. Then, from the point of view of the transient energy of the system, the influence of the equivalent proportional coefficient of virtual inertia control of the DFWG in two regions on the transient energy during the acceleration and deceleration of the system is studied when the swing direction of the system power angle is different, and the influence mechanism of the swing direction on the frequency stability is further investigated. Finally, the maximum frequency offset is proposed to evaluate the frequency stability of the system, and the two-region system simulation model is built into the PSASP 7.4.1 simulation software to verify the correctness of the proposed theory.

Fault current limitation approaches for grid-following controlled DERs in microgrid environments

The adoption of distributed energy resources in the electricity system is rapidly spreading worldwide, where power converters are the preferred method for better integration. However, the fault behaviour of these converter-integrated distributed energy resources is still uncertain and varies according to the considered model representation. Furthermore, the fault response of the converter-integrated resources is influenced by the thought control strategy, which adds complexity to the analysis. Aimed at a better understanding of the converter-integrated resources fault behaviour, this document presents an analysis of the fault response considering active-reactive power control as the main control strategy of the converter-integrated resources; different current limitation strategies are tested and linked to physical constraints of the power electronic converters, also aligning with the reactive power support during fault. The fault behaviour is studied in a microgrid operating in grid-islanded and grid-connected modes, where the obtained results validate the adequacy of the fault current limitation strategies by compelling the converter-integrated resource to have a more realistic behaviour. Additionally, this study investigates the impact of prioritising direct or quadrature current injection (id or iq) during fault. Lastly, this study demonstrates that the converter-integrated resource can adequately support the microgrid voltage while complying with the reactive power generation requirements, which are usual in national grid codes and not often considered in the revised fault current limiting strategies. Overall, this study provides several insights into the performance of converter-integrated resources under fault conditions while emphasising the importance of fault-current limitation strategies to assure a reliable and realistic microgrid operation. These findings affect modern power systems designs, analysis, and implementation of power control strategies.

Microgrid based on ICA and improved cuckoo search algorithm optimizing scheduling methods

Abstract The optimal operation of microgrids can provide a strong guarantee for the efficient utilization of distributed power and renewable energy. To solve the important problem of optimized operation of microgrids with a large proportion of distributed power sources, a microgrid optimal scheduling and operation method is proposed in the paper. This method first establishes a microgrid model containing distributed power sources and uses the ICA algorithm to screen out the key factors in the economy of the grid system. Then, taking the construction and operation cost of the microgrid as the objective function, it proposes to solve the model based on the ICA and the optimized cuckoo search algorithm. The method is able to effectively schedule the microgrid objective function based on the conditions, with the ability to quickly solve the scheduling problem and greatly reduce the system’s electricity cost. It can greatly increase the utilization rate of photovoltaic power generation. The method has a small amount of computation, small storage, and simple operation. An empirical study is carried out on the designed microgrid optimal scheduling scheme.

Robust optimization scheduling of microgrid considering a tiered carbon trading mechanism and flexible loads

Abstract Considering the comprehensive demand response, stepwise carbon trading, and uncertainty of wind power output for cogeneration microgrids, relevant optimization scheduling research will be carried out. The electric heating load is divided into non-dispatchable electric heating load and dispatchable electric heating load. By introducing electric heating demand and tiered carbon trading, the economic and low-carbon operation of microgrids can be achieved. The fluctuation of the output of new energy units is reflected using probability scenario sets in statistics, and a robust optimization model with related distributions is constructed using the C&CG algorithm to calculate and solve the established model. The simulation and comparison of numerical examples show that the microgrid energy management scheme formulated by the model established in this paper has good economic and environmental friendliness, and it also simultaneously validated the superiority of distributed robust optimization methods over traditional optimization methods

Steady state stability assessment of voltage source converter in a DC microgrid under weak grid conditions

Traditionally, steady-state assessment involves analyzing numerous variables using Eigen analysis. This paper presents a decision support application for diagnosing the steady-state assessment of droop-controlled voltage source inverters in islanded microgrid operations or weak grid operations with reduced input attributes. This paper proposes an approach using feature extraction from the state space variables of the droop-controlled voltage source inverter (VSI). Photovoltaic (PV) and wind energy sources are considered with their stipulated power-delivering capability considered. To improve the generalization of the predictive model, preprocessing techniques are employed to eliminate data distortions. Dimensionality reduction is achieved through principal component analysis (PCA) applied to the steady-state variables. The evaluation of the VSI's steady-state stability is conducted utilizing support vector classification algorithm. To ascertain the reliability of the steady-state stability classification, an assessment of the support vector machine (SVM) model's performance is carried out, which includes the examination of metrics like the area under the curve (AUC) and the receiver operating characteristics (ROC) curve. The findings from the assessment of VSI's steady-state stability indicate a commendable level of performance, achieving an accuracy rate of 93.5%.

Industrial multi-energy communities as grid-connected microgrids: Understanding the role of asymmetric grid-charge regulation

The industrial sector is currently the leading emitter of greenhouse gases worldwide. Lowering emissions, the collaborative use of energy and storage technologies in Industrial Energy Communities (IEC) is a promising option, typically implemented as a grid-connected microgrid. To support successful implementations of IECs, it is essential to understand not only the interaction of different technical assets within an IEC but also the corresponding regulation that determines the IEC's economic and ecological performance. Similar to different technical capabilities of available assets, companies of an IEC are typically affected by regulation in different, asymmetric ways. To the best of our knowledge, we are the first to investigate the economic and ecologic effects stemming from asymmetric regulation, i.e., regulation that differs between different participating companies via a microgrid approach. By developing a novel linear model for German asymmetric grid charge regulation, we are able to optimize the economic operation of complex multi-energy microgrids under detailed regulatory conditions. In more detail, we formulate and implement a mixed-integer linear program to investigate the joint operation of a multi-energy IEC under asymmetric regulation. We conduct a real-world case study to evaluate the effects of German grid-charge regulation as a significant example of asymmetric regulation and compare the results of our IEC to a situation where every company of the IEC manages its assets individually. Our results indicate that IECs have the potential to significantly reduce the total operational energy costs under the current asymmetric German grid-charge regulation. While the shared assets see a higher utilization in the IEC, the impact on emissions is, however, limited.

Comparative analysis and implementation of DC microgrid systems versus AC microgrid performance

DC power systems have emerged as a cost-effective solution for electric power generation and transmission, challenging the dominance of AC distribution systems. However, a comprehensive efficiency comparison between DC and AC microgrids remains understudied. This study seeks to explore and conduct a thorough survey on development and designing of DC microgrids to address this gap. Firstly, a comprehensive literature review comparing the efficiencies of AC and DC microgrids has been presented. The analysis highlights the superior efficiency of DC distribution systems over AC systems, supported by detailed advantages. Secondly, hardware implementation has been performed to directly compare the efficiency of DC versus AC systems. Research validity and application are further improved by the hardware prototype’s scalability, which in simulation allows for a thorough assessment of system stability over a range of scenarios from four to six terminals. Test results from the built hardware prototype demonstrate an astounding 15% increase in efficiency using the DC system compared to the AC system, demonstrating its potential for improved performance in real-world scenarios. In simulation results, the designed DC microgrid demonstrates stable voltages of 500V under steady state operation and rapid recovery within 80 ms under both symmetrical and asymmetrical faults has been observed. The research being investigated utilizes hardware implementation and simulation to provide useful insights into the efficiency and stability of DC microgrids in comparison to AC systems. These results are important for developing robust power distribution networks in modern energy environments, promoting sustainability and dependability in infrastructure growth.

Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

A multi-stage framework for coordinated scheduling of networked microgrids in active distribution systems with hydrogen refueling and charging stations

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Operation characteristics analysis and optimal dispatch of solar thermal-photovoltaic hybrid microgrid for building

The optimal dispatch for hybrid microgrids is the crucial approach to decrease maintenance costs and enhance operational reliability. This paper aims to provide a feasible solution for the optimal dispatch of a solar thermal-photovoltaic hybrid microgrid. A distributed energy system of a building is established and the power load is analyzed. Operation parameters are optimized for hybrid microgrid in isolated operation mode and grid-connected operation mode. The operation performance with adequate and inadequate solar irradiation is evaluated by using fixed dispatch and optimal dispatch strategy. In addition, the performance of hybrid microgrid under power selling and purchasing conditions are analyzed. Results show that the operation mode of building hybrid microgrid can be adjusted to isolated/grid-connection modes based on actual power requirements. The optimal dispatch strategy shows great advantages on both solar adequate and inadequate conditions under isolated operation mode for hybrid microgrid, at most 14.3 % lower for maintenance than that of fixed strategy. In addition, the power selling to the external power grid can significantly improve the profit of the hybrid microgrid and generate earnings of $6102.1 per year under optimal dispatch. The research can offer an analysis approach for hybrid microgrid, and further increase clean energy utilization efficiency.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Study on Master-Slave Game Optimization Operation of Integrated Energy Microgrid Considering PV Output Uncertainty and Shared Energy Storage

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO.

A Comprehensive Review of Existing and Pending University Campus Microgrids

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses’ microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation.

The Development Plan for Microgrids Encompasses Engineering, Economics, and Practical Knowledge

This article describes the progress of the microgrid development process, including the completion of the initial guideline. It shows the main points that need to be realized, developed and used for the operation of a microgrid; In other words, a microgrid is a distribution system with many electrical devices that can be connected to a large grid or operate independently. The paper also discusses different types of microgrids, provides methods for estimating their effectiveness, reviews microgrid performance and technology, and documents the microgrid pilot program.

Real-Time Microgrid Energy Scheduling Using Meta-Reinforcement Learning

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids’ efficient, stable, and economical operation in their initial stages.

Maximum span determination and optimal sizing of cable for improved performance of droop-controlled DC microgrid

DC microgrids are seen as smart solutions to interface DC loads and distributed energy resources (DERs). However, in DC microgrids, the placement of sources and loads as well as the size of cable impact the system’s span, regulation of voltage, and losses. This paper proposes novel algorithms to determine the maximum span and optimal size of cable for the improved performance of a droop-controlled DC microgrid. The proposed algorithms utilize an improved power flow analysis (IPFA) method based on Newton-Raphson technique to determine the maximum span and the optimal size of cable, enhancing the voltage regulation and reducing the cost. Additionally, the impacts of power rating, droop constants, and size of cable on the DC microgrid’s maximum span are investigated and reported. Owing to the intricate nature of the problem concerning the ideal size of cable for the droop-controlled DC microgrid, a heuristic optimization approach is employed. Further to enhance the rate of convergence and computational performance, an improved particle swarm optimization (IPSO) is also proposed. The constraint of keeping the bus voltage variations below the allowable voltage regulation limits is applied to the objective function of the optimization problem. In the case of a droop-controlled, DC microgrid having a specified configuration, the suggested algorithm can determine the ideal size of cable, guaranteeing both the least cost and enhanced voltage regulation. Comprehensive numerical and modelling results have been presented for a droop-controlled DC microgrid with different loads and DERs to verify the effectiveness of the proposed methods. Furthermore, the analysis reveals that configuration III of the DC microgrid with an optimal cable size of 19 mm2 lowers the absolute voltage regulation to as low as 1.06 V. The results of the detailed analysis validate the enhanced performance of the proposed algorithms and are highly useful to both system designers and consumers of the DC microgrids, eventually paving the way for the widespread use of DC microgrids in the future.

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Model predictive control-based energy management system for an isolated electro-thermal microgrid in the Amazon region of Ecuador

Access to electricity is a fundamental key to developing countries' growth. Microgrids (MG) have become potential solutions to provide energy to isolated rural areas in a safe and environmentally friendly way. Therefore, proposals for alternative solutions to bridge the electrification gap in the Ecuadorian Amazon – which has the most significant percentage of its population without access to electricity – are of significant interest. Consequently, this paper presents the design of an Energy Management System (EMS) based on Model Predictive Control (MPC) for an isolated electro-thermal microgrid comprising a photovoltaic generator, a diesel generator, a lithium-ion battery Energy Storage System (ESS), electrical loads, and a domestic hot water system. The EMS aims to supply energy reliably and safely, minimize the MG's operation costs, and extend the ESS's useful life while satisfying the end users' comfort. This study includes an estimated degradation model for the ESS’s State of Health (SOH), an essential parameter contributing to reducing the microgrid's operating costs in the long term. Simulation results using one-year data present the influence of the prediction horizon on the MG’s scheduling. In addition, the benefits of the proposed EMS are highlighted by comparing it with the results achieved by a Unit Commitment approach, where it is demonstrated that the proposed EMS presents a reduction in MG operating costs and greenhouse gas emissions while maximizing the utilization of renewable energy and extending the lifespan of the ESS. Finally, an experimental validation, using a Typhoon Hardware-in-the-loop HIL-402 device in real-time operation, stands out the effectiveness and feasibility of the proposed MPC-based EMS.