Optimal Energy Management Problem Research Articles

Energy management strategy for hybrid power ships based on nonlinear model predictive control

How to achieve energy saving and emissions reduction is a critical issue of great concern to all countries. The development of marine hybrid power technology provides a new research direction for rational energy distribution and utilization. In this paper, an energy management strategy for real-time regulation of internal combustion engines and motor torque is designed to solve the optimal energy management problem of parallel hybrid power ships. A nonlinear model predictive control method is used as the core, aided by a machine learning technique and an unscented Kalman filter. Machine learning techniques are used to extract representative test cycles and apply them to the testing and evaluation of design strategies. The effect of random waves on propeller load torque is observed using an unscented Kalman filter, which is used as a disturbance variable to participate in establishing the prediction model. Furthermore, a trade-off coefficient is designed to further demonstrate the balance between fuel consumption and carbon emission optimization. To verify the performance of the designed energy management strategy, NMPC is compared with the conventional intelligent optimization algorithm and dynamic programming algorithm, and the fuel consumption and carbon emission trade-off parameterization of the strategy is investigated. The results show that the strategy can resist random wave disturbances and follow the reference data of the ship’s internal combustion engine speed extracted by machine learning. The energy distribution is also performed according to the variation of the trade-off coefficients to achieve the optimal trade-off energy management under the required constraints.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control

Marine hybrid technology is attracting increasing research interest thanks to its power versatility and potential economic advantages. However, its overall quality performance is determined by the energy management strategy. This research deals with a variable weighted decision model predictive control (VWDMPC) for marine hybrid energy management, where the main task is to dynamically weight-tuning and optimally allocate energy to obtain the optimal trade-off in terms of fuel consumption, power performance, and energy allocation. For the sake of enhancing the real-time and adaptability of VWDMPC, we utilize the KKT condition to compress the optimal energy management problem and the weight tuning process in one, which cleverly simplifies the optimization process. Performance experiments are performed on a test bench and a real-time hardware execution platform. Different weighting decision comparison scenarios were designed to evaluate the power performance improvement and equivalent fuel consumption of the proposed VWDMPC and give the best proportional weights (0.33,0.27,0.2,0.2) that take both aspects into account. Moreover, this paper also provides validation and feedback based on the evaluation results, which show that the optimal combination of weights that take into account economy and dynamics ranges from (0.3,0.3,0.2,0.2)-(0.33,0.27,0.2,0.2) based on the radar plot drawn from the performance index parameters.

Towards Lower Carbon Emissions: A Distributed Energy Management Strategy-Based Multi-Objective Optimization for the Seaport Integrated Energy System

To decrease fuel-based energy consumption, it is important to investigate the optimal energy management problem for the seaport integrated energy system in a fully distributed manner. A multi-objective energy management model is constructed, considering energy consumption, greenhouse gas emission, and carbon trading, which satisfy the sustainable development of the international shipping industry advocated by the International Maritime Organization (IMO). The carbon exhaust of a seaport is restrained by integrated carbon capture/storage devices. A fully distributed energy management strategy with dynamic-weighted coefficients is proposed to acquire the optimal solutions for the seaport integrated energy system. Moreover, to decrease the communication resources, an event-triggered mechanism between energy bodies was designed against the bandwidth limitation of the seaport. Finally, a rigorous mathematical analysis based on the multi-agent theory and the case studies proves the effectiveness of the proposed method.

A chaotic sine cosine algorithm with crossover operator for microgrid energy scheduling considering uncertainty

Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem.

Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference

The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-min data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system.

Impact of decentralized microgrids optimal energy management on power system dynamics

This paper proposes a unified framework between the optimal energy management (OEM) problem of grid-connected microgrids and the frequency control of power systems through time-domain simulations. The proposed formulation is aimed at analyzing how different objectives for the microgrid OEM and control strategies affect the system frequency regulation. Each OEM model minimizes the microgrid local objective while ensuring energy reserves and operational limits by solving a two-stage stochastic optimization mathematical model. Two control approaches of microgrids are considered, namely, greedy and cooperative. The proposed framework is evaluated considering the IEEE 39-bus test system, including multiple microgrids with different energy storage capacities, and control strategies. Results show an improvement in system’s frequency deviation when MGs operate in the cooperative configuration while simultaneously reducing their operational costs.

Load forecasting and risk assessment for energy market with renewable based distributed generation

This paper projects the risk associated with social benefit gained by Independent System Operator (ISO) due to unpredictability of load demand and intermittent nature of Renewable Energy Sources (RES). The hybrid method i.e. Wavelet Transform–Auto Regression Integrated Moving Average (WT-ARIMA) is used for forecasting the load in day-ahead market. The load demand for 24 hours is forecasted using the historical hourly data of 2 months. The mean absolute percentage error (MAPE) is calculated for both classical ARIMA and WT-ARIMA model. When compared to the ARIMA model, the WT-ARIMA model has a better fit to the time series data. This paper relates the load forecasting with energy market and shows the impact of forecasting error on market parameters. The system parameters like generation cost, social benefit and power loss are analyzed in day-ahead and real-time market. This work also elaborates the effect of intermittent nature of RES on the system parameters. The optimal location of RES based distributed generation is identified by higher Locational Marginal Price (LMP) based ranking method. The optimal energy management problem is formulated with the aim to minimize the total generation cost taking into account the variability in wind power and solar power. The uncertainty modeling of wind and solar is projected by the Rayleigh and Beta distribution respectively. The risk associated with social benefit of ISO due to uncertainty of wind and solar is calculated using indices, Value at Risk and Conditional Value at Risk. The proposed approach is implemented on IEEE 30 bus system.

Privacy-Preserving Optimal Energy Management for Smart Grid With Cloud-Edge Computing

Optimal energy management of smart grids requires the information exchange between devices, which may disclose private information to the adversaries and further lead to great losses. To this end, this article considers the privacy-preserving optimal energy management problem for smart grids, which integrates both the power allocation of distributed energy resources on the supply side and the demand response of distributed load demands on the demand side. We first propose a cloud-edge computing structure of the smart grid and model the optimal energy management problem as the maximization problem of social welfare including the supply-side net benefit and the demand-side net utility, while maintaining the supply–demand balance and satisfying the operating constraints. A privacy-preserving average consensus algorithm is then developed, where each node sends the projected states to their neighbors to protect the privacy of the initial state. By applying the privacy-preserving average consensus algorithm, we propose a distributed privacy-preserving optimal energy management algorithm based on the generalized alternating direction method of multipliers. Finally, simulation examples are provided to validate the effectiveness of the proposed algorithms.

Enhanced Battery Power Constraint Handling in MPC-Based HEV Energy Management: A Two-Phase Dual-Model Approach

This article presents a novel two-phase dual-model approach to more appropriately handle battery power constraint in model predictive control (MPC)-based hybrid electric vehicle (HEV) energy management. The approach fuses two typical battery models with different fidelities in two consecutive phases of MPC's prediction horizon, where more accurate first-order resistor-capacitor (RC) model is adopted in the near phase, while a purely ohmic impedance model is used in the more distant phase. More importantly, it is mathematically rigorously proved that the first-order RC model can be reformulated as an equivalent single-state model while preserving accurate voltage calculation, provided that the length of phase one is properly determined. Finally, the formulated optimal energy management problem is solved using a forward dynamic programming (FDP) algorithm. Both numerical examination and model in the loop (MiL) test using an experimentally validated forward-facing simulator demonstrate that compared with traditional approach, the proposed approach can achieve better optimality in the condition of very sparse state discretization, which is imperative for real-time implementation.

Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

Microgrid (MG) is an effective way to integrate renewable energy into power system at the consumer side. In the MG, the energy management system (EMS) is necessary to be deployed to realize efficient utilization and stable operation. To help the EMS make optimal schedule decisions, we proposed a real-time dynamic optimal energy management (OEM) based on deep reinforcement learning (DRL) algorithm. Traditionally, the OEM problem is solved by mathematical programming (MP) or heuristic algorithms, which may lead to low computation accuracy or efficiency. While for the proposed DRL algorithm, the MG-OEM is formulated as a Markov decision process (MDP) considering environment uncertainties, and then solved by the PPO algorithm. The PPO is a novel policy-based DRL algorithm with continuous state and action spaces, which includes two phases: offline training and online operation. In the training process, the PPO can learn from historical data to capture the uncertainty characteristic of renewable energy generation and load consumption. Finally, the case study demonstrates the effectiveness and the computation efficiency of the proposed method.

Review and clustering of optimal energy management problem studies for industrial microgrids

The optimal energy management problem of microgrids is an agenda item for the energy field. Especially industrial microgrids, whose energy demand is high and uninterruptible, need to be managed by a holistic view of optimization. Technical researches in this field are to be enriched with the literature review that looks through various angles. In the scope of this paper, the research literature is reviewed deeply and analyzed with the aim of presenting the gaps. Articles written in English, in the period of 2010 to 2019 have been revised thoroughly. By eliminating the search results, 105 relevant articles have been determined. Objective(s), criteria for analysis, processes included, power source(s), storage system, other system components and methodology of each article have been studied. The literature has been summarized, it has been seen that solar, wind, and diesel sources are used in about 82%, 55%, and 16% of articles, respectively. Then, they have been clustered using self-organizing maps. Nine clusters have been obtained which separate the articles clearly. Especially, three of them which have fewer data have been analyzed. Clustering results have been interpreted and utilized to show the research openings. Achievements of this study will lead the scientific research that will fill in the gaps.

Optimal Denial-of-Service attack energy management against state estimation over an SINR-based network

We consider a scenario in which a DoS attacker with the limited power resource and the purpose of degrading the system performance, jams a wireless network through which the packet from a sensor is sent to a remote estimator. To degrade the estimation quality most effectively with a given energy budget, the attacker aims to solve the problem of how much power to obstruct the channel each time, which is the recently proposed optimal attack energy management problem. The existing works are built on an ideal network model in which the packet dropout never occurs when the attack is absent. To encompass wireless transmission losses, we introduce the signal-to-interference-plus-noise ratio-based network. First we focus on the case when the attacker employs the constant power level. To maximize the expected terminal estimation error at the remote estimator, we provide some more relaxed sufficient conditions compared with the existing work for the existence of an explicit solution to the optimal static attack energy management problem and the solution is constructed. For the other important index of system performance, the average expected estimation error, the associated sufficient conditions are also derived based on a different analysis approach with the existing work. And a feasible method is presented for both indexes to seek the optimal constant attack power level when the system fails to meet the proposed sufficient conditions. Then when the real-time ACK information can be acquired, a Markov decision process (MDP) based algorithm is designed to solve the optimal dynamic attack energy management problem. We further study the optimal tradeoff between attack energy and system degradation. Specifically, by moving the energy constraint into the objective function to maximize the system index and minimize the energy consumption simultaneously, the other MDP based algorithm is proposed to find the optimal dynamic attack power policy which is further shown to have a monotone structure. The theoretical results are illustrated by simulations.

A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation

This article presents a stochastic framework in probabilistic optimal energy management (OEM) of renewable-based microgrid (MG) incorporating uncertainty. OEM of MG is an optimization problem to optimize functional cost of energy associated with distributed generation and responsive loads. Effective coordination between power generation and consumption becomes an essential assignment in MG operation that should be done at a minimum cost and proper scheduling. Renewable energy, load demand and market bids are random which requires a computationally amenable robust uncertainty model to deal with its uncertainty effects. Hence, OEM problem has been formulated using k-point estimation method (k-PEM) based on moments of input random variable (IRV). Inherent randomness associated with renewable energy parameters ascends the MG problem to be stochastic which requires a powerful heuristic tool to obtain optimal operating schedules and cost estimation. Hence, modified glowworm swarm optimization (MOGSO), a bio-inspired algorithm based on swarm intelligence is proposed to solve optimization functions globally in both deterministic and probabilistic approaches. In order to visualize the effectiveness of proposed hybrid PEM-MOGSO method, the MG OEM problem is solved considering three different scenarios to scrutinize its robustness in manipulating optimal operating schedules and statistical cost estimates.

The optimal management of the prosumer’s resources via stochastic programming

This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving.

Optimal energy management in the smart microgrid considering the electrical energy storage system and the demand-side energy efficiency program

Smart MicroGrids (MGs) are known as a powerful platform for exploiting the Electrical Energy Storage Systems (EESSs). On the other hand, the Energy Efficiency Programs (EEPs) are recognized as an integral and highly valuable element of smart MGs investments and operations. While the EEPs are known to be long-term programs, they affect the short-term programs such as day ahead energy management. In this paper, the optimal energy management program model associated with EESSs and EEPs namely EMPEESSsEEPshas been proposed. The problem takes the investment rate on the EEPs into account while solving optimal energy management problem. To do this, the EEPs have been applied to the demand model. Furthermore, the proposed demand model has been used in the optimal energy management of the smart microgrids. The proposed objective function has been modeled as Mixed Integer Non-Linear Programming (MINLP) for the optimal energy management. Moreover, the GAMS software is used to solve the formulated optimization problem. The results of different scenarios confirm that the EEPs and EESSs are effective programs for the smart MGs energy management. The results are analyzed, and the best cost optimal solution is identified.

Cybersecurity Enhancement of Power Trading Within the Networked Microgrids Based on Blockchain and Directed Acyclic Graph Approach

Power grid resilience, reliability, and sustainability can be improved significantly by decomposing the large grids into networked microgrids (NMGs). However, the optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose a secured stochastic energy management framework for NMGs based on the modified blockchain approach, utilizing the directed acyclic graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security and lower risks within the network, thus eliminating the financial fraud and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models, mainly due to the storage and high complexities of hash address calculations, this paper proposes a new modified blockchain technology based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. The unscented transform (UT) approach is employed to model the uncertainties of forecast error in hourly load demand, solar power output, and wind turbines power output. Finally, the proposed model is tested on an NMG system with four MGs, including two residential MGs (as the noncrucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads).

Fast Dual-Loop Nonlinear Receding Horizon Control for Energy Management in Hybrid Electric Vehicles

This paper proposes a receding horizon optimization strategy for the problem of energy management in plug-in hybrid electric vehicles. The approach employs a dual-loop model predictive control strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized projected Newton method. The controller is compared with existing approaches based on Pontryagin’s minimum principle and the effects of imprecise knowledge of the future driving cycle are discussed. This paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Second, the effects of imprecise knowledge of the future driving cycle are illustrated.

Stochastic Multi-Timescale Energy Management of Greenhouses With Renewable Energy Sources

In this paper, we propose a stochastic multi-timescale energy management scheme of greenhouses with renewable energy sources (RES), including a photovoltaic (PV) system, combined heat and power (CHP) unit, and energy storage systems. The optimal energy management problem is formulated as a multi-timescale Markov decision process to address the randomness of RES and the outside weather conditions. In particular, a fast-timescale process is used to model the rapidly changing electrical process with fine granularity, while a slow-timescale process is used to model the gradually varying thermal process to reduce the computational complexity. Exact solution of the optimal energy management problem is derived to minimize the cost of greenhouse operation. To further reduce the computational complexity of finding the optimal energy management policy, an approximation solution is also derived. The proposed energy management scheme is evaluated based on a commercial greenhouse structure from Bonnyville Forest Nursery, Inc. for spruce, as well as real data of weather conditions, PV generation, CHP unit, and energy storage systems.

Convex modeling for optimal battery sizing and control of an electric variable transmission powertrain

This paper provides convex modeling steps for the problem of optimal battery sizing and energy management of a plug-in hybrid electric vehicle with an electric variable transmission. Optimal energy management is achieved by a switched model control, with driving modes identified by the engine on/off state. In pure electric mode, convex optimization is used to find the optimal torque split between two electric machines, in order to maximize powertrain efficiency. In hybrid mode, optimization is performed in a bilevel program. One level optimizes speed of a compound unit that includes the engine and electric machines. Another level optimizes the power split between the compound unit and the battery. The proposed method is used to minimize the total cost of ownership of a passenger vehicle for a daily commuter, including costs for battery, fossil fuel and electricity.