Distributed Model Predictive Control-Based Power Management Scheme for Grid-Integrated Microgrids

This work presents a learning-based approximation scheme to improve the computational burden of general distributed model predictive control (DMPC). Under the framework of dual decomposition, an independent neural network approximator with rectified linear unit is designed for each subsystem. The primal and Lagrangian dual analysis indicates that this error-containing approximation is a suboptimal solution of the global DMPC optimization problem. In addition, the distributed conditions designed to guarantee the feasibility and stability of global system, which inspired by an explicit-implicit procedure to approximate an MPC law, are derived from an decoupling process using dual decomposition. In cases with infeasible approximator output or the distributed conditions are violated, an backup controller will used to promote the implementation of approximation. The proposed learning-based DMPC approximator with feasibility and stability guarantees is finally employed to a reactor-separator process, and simulation results demonstrate the efficiency and superior performance of proposed strategy.

In order to reduce the heavy communication burden of cooperative distributed model predictive control (DMPC), A DMPC algorithm based on event-trigger is proposed to balancing the control performance and communication burden through optimizing the interconnection structure of subsystems. An event-trigger using the system Lyapunov function is constructed to avoid solving the introduced MINLP optimization directly, the optimal interconnection structure will be updated only when the event-trigger is triggered. The feasibility of the proposed algorithm in system performance and communication burden decrease is validated by the load frequency control (LFC) problem simulation of interconnected power system.

The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

In distributed model predictive control (DMPC), where a centralized optimization problem is solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.

With the high penetration of renewable energy, the active distribution network (ADN) and multi‐microgrids (MMGs), as emerging multi‐layered energy management systems, face challenges such as voltage violations and conflicts of interest among multiple agents. To address these distributed coordinating optimization issues in the ADN‐MMGs system, this paper proposes a coordinating optimization method for ADN‐MMGs systems that balances voltage robustness and system economics. In the day‐ahead stage, the method considers MMGs' participation in the voltage regulation ancillary service market and decentralized peer‐to‐peer (P2P) trading market, and baseline plans are developed for voltage stability and economic operation. In the intra‐day stage, ADN and MMGs are treated as subsystems. Using voltage sensitivity matrices and consensus algorithms, a state‐space‐based distributed model predictive control (Di‐MPC) coordination model is constructed, effectively mitigating short‐term power fluctuations from renewable energy and load variability. An analysis on a modified IEEE 33‐bus system demonstrates that the proposed method enhances economic efficiency and voltage robustness by coordinating subsystem energy flows. Compared to the distributed economic model predictive control (MPC) scheme, the proposed method reduces the state of charge (SOC) tracking error of MMGs by 51.21% and ensures that the deviation in planned daily operating cost for the ADN‐MMGs system does not exceed 1%.

To optimize fuel economy for platooning plug-in hybrid electric trucks, this paper proposes a co-optimization framework that integrates cooperative adaptive cruise control and energy management to enhance driving safety and fuel efficiency in complex traffic environments. The control strategy is divided into two layers: in the upper layer, a cooperative adaptive cruise control model based on distributed model predictive control (DMPC) is used to achieve stable platoon following and vehicle spacing, thus improving the overall platoon efficiency. In the lower layer, a distributed soft actor-critic (DSAC) algorithm is used for the fine-grained power distribution of plug-in hybrid electric trucks, enabling efficient energy utilization. The results demonstrate that this strategy significantly enhances the fuel economy and vehicle-following performance of plug-in hybrid truck platoons. Compared with the classical deep deterministic policy gradient (DDPG) algorithm, the energy management strategy based on the distributed soft actor-critic offers higher computational efficiency.

Model predictive control power management strategies for HEVs: A review

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

There is an era started in the field of power energy (PE) management in the electric vehicles (EVs) application cloud, which impacts the smart grid in significant ways and necessitates the collaboration of multiple branches of engineering. Most of the problems with EVs stem from their limited range, which can be improved by incorporating additional forms of energy storage. This paper makes an effort to bring a fresh viewpoint to the description of the power and energy management problem facing EV, taking into account all the needs of such a vehicle. Using a novel power and energy management system, the proposed methodology enables a systematic approach to this multidisciplinary problem. Implementing a power and energy management system for a dual-source EV using lead-acid batteries and ultra-capacitors (UCs) exemplifies the novel framework's capabilities. The electronic power development architecture is outlined in detail, along with the implementing modular blocks that make up the entire system architecture.

Worldwide, the current energy system increasingly shows economic, social and environmental drawbacks and it does not appear to be sustainable. Among the most pressing problems are climate change due to CO2 emissions, the depletion of fossil resources, the security of energy supply, and threats to economic development. These problems can be addressed by moving towards energy systems that increasingly rely on renewable and sustainable energy sources. At the same time, the rapid progress in renewable energy harvesting and information and telecommunication technologies offer unprecedented opportunities for energy generation, distribution and management, and to more strongly involve people in the process of energy generation and management, which is needed to further enhance the sustainable energy systems. Indeed, renewable and sustainable energy sources are likely to be produced and distributed in a decentralised manner, and require energy management systems that differ greatly from the current centralised system. Hence, the integration of renewable sources has major implications for the design and management of energy systems, relying on innovative ICT solutions and stronger user involvement that facilitate supply and demand matching. Effective energy management requires the active participation and support of end users, who should accept the energy management systems, and adapt their energy use to the available demand as to enhance the efficiency and stability of the energy system. Hence, the design of future smart grids should be based on sound behavioural models, which should be integrated in ICT centred solutions that facilitate supply and demand matching. More specifically, we need to understand how truly complex information on energy production, use and storage can be effectively communicated among relevant actors, and what type of information and incentives should be provided to realise effective supply and demand matching. In addition, we need to understand how to take into account cultural specifics in the design of energy management systems, to enhance effective energy management across the world. The active and aware involvement of the end-user can only be achieved if a scalable and reliable ICT infrastructure is in place, which naturally interfaces the energy flows with the decision autonomy of the people. Hence, the design of effective energy management systems should be take into account key behavioural as well as ICT requirements in an integrated way. Our proposal is thus to investigate (1) ICT based solutions for monitoring and controlling the power grid focusing on the distribution networks (medium-low voltage) and buildings (offices and residential complexes) specific to the Qatar context; (2) behavioural models on effective and acceptable incentives to match energy supply and demand; (3) cultural specificities in the design of energy management systems by comparing results from research in Qatar to similar research in other parts of the world, in particular Europe. Together, these will provide important insights in how to promote efficient and stable renewable energy systems that meet Qatar's sustainability goals.

Hierarchical energy management control based on different communication topologies for hybrid electric vehicle platoon

Model predictive control for energy management in buildings Part 2: Distributed Model Predictive Control

Automated power and energy management systems (APEMS) are unifying control systems that coordinate individual power system components and present appropriate information to system operators. This paper presents ongoing work to establish a methodology and framework for evaluating such systems. As the design space offers many degrees of freedom including functional decomposition, choice of sensors, means of communication, and controls, systematic exploration and comparison of viable options is critical to designing a solution that maximizes overall system performance. This paper presents on initial progress towards a new unified, flexible, and scalable methodology that effectively evaluates performance and objectively contrasts candidate APEMS solutions.

Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions

A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark