Optimal Energy Management Strategy Research Articles

Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in the PV power station grid-connected regulations, the maximum power point tracking working point switching control is performed for the PV power station, from which the grid-connected PV power and HESS power are obtained. Then, a capacity optimal allocation method and frequency division energy management strategy (EMS) for HESS is proposed to find the energy response and power response of each energy storage source. Furthermore, a multi-objective optimization function with HESS cutoff frequency as the independent variable is constructed, and the input cost of HESS and the life loss of the lithium battery are optimized. Finally, the overall strategy is compared and analyzed under the scenarios of three typical PV power fluctuations. Simulation results show that the control strategy has a good smoothing effect on PV power fluctuations. From the perspective of the annual comprehensive input cost, HESS realizes the optimal capacity allocation when the cutoff frequency is 0.0066 Hz.

Research on Global Optimal Energy Management Strategy of Agricultural Hybrid Tractor Equipped with CVT

This paper presents a proposed global optimal energy management strategy based on dynamic programming to enhance the energy consumption efficiency of an agricultural hybrid tractor that is equipped with a continuously variable transmission (CVT). Firstly, using a diesel-electric parallel agricultural hybrid tractor as the research object, a tractor-rotary tillage coupling dynamics model is constructed. Secondly, with the torque and speed of the motor, the torque and speed of the diesel engine, and the CVT speed ratio as the control variables, the state of charge (SOC) of the power battery as the state variable, and the goal of minimizing the total energy consumption of the whole machine, a global optimal energy management model based on dynamic programming is established. Finally, the field operation measured data is injected into the MATLAB simulation model, and experiments are carried out to verify the effectiveness of the energy management strategy. The results show that compared with the power-following energy management strategy, the proposed energy management strategy can make the diesel engine and electric motor work in the optimal area, and effectively reduce the total cost of energy consumption of the tractor during field operations. Under the condition of rotary tillage, the total cost of energy consumption is decreased by 16.89%.

A new optimal energy management strategy of microgrids using chaotic map‐based chameleon swarm algorithm

AbstractThis study provides an optimal and efficient energy management strategy (EMS) for the cost‐effective performance of a combined solar and green energy microgrid in both independent and grid‐connected modes. A microgrid is formed by the system that includes a fuel cell (FC), a battery storage (BS), and a photovoltaic system (PV). Evidently, the unguaranteed features of the renewable energy and load electricity generate instability problems as well as economic ones, like operational expenses. To tackle these issues, a novel procedure is proposed that has been improved by a modified metaheuristic algorithm, called chaotic map‐based chameleon Swarm Algorithm (CCSA). In this method, the simulation is based on a one‐day planning perspective. The method aims to supply the power requirements of the load at the lowest possible cost under a constant DC bus voltage, protect the battery from overcharging and depletion, and improve the efficiency of the total system. To illustrate the suggested method's effectiveness, the simulation results of CCSA are compared with some studied methods in the literature, including GAMS, bald eagle search optimization algorithm (BEOA), original chameleon swarm algorithm (CSA), and grey wolf optimization algorithm (GWOA).

Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

The fuel economy of plug-in hybrid electric bus (PHEB) is highly dependent on its energy management strategy (EMS). In practice, the fuzzy control (FC) is widely used in EMS due to its real-time performance and robustness. However, the FC with fixed parameters is difficult to obtain the optimal fuel economy under changing traffic conditions. Regarding this, the control parameters of FC need to be optimized, but this scheme needs to overcome the subsequent calculation burden and time consumption. Therefore, the design of a real-time EMS with parameter optimization is a challenging problem. Inspired by this issue, a cyber-physical optimization-based fuzzy EMS is proposed in this paper. Firstly, a cyber-physical system framework is formulated for PHEB to eliminate the conflict between parameter optimization and real-time operation of EMS. Secondly, considering the uncertainty of the vehicle environment, an IT2 FC with optimization parameters is designed for real-time torque allocation. Thirdly, an iterative modified particle swarm optimization (IMPSO) algorithm is proposed to optimize parameters to accurately and quickly converge to the optimal solution. Additionally, the optimization problem with multi-objective that takes battery life into account is introduced. Finally, simulation and hardware in loop test are used to discuss the performance of the proposed EMS. The results reveal that the IMPSO algorithm can improve the optimization effect. Compared to conventional rule-based and fuzzy-based strategies, the proposed EMS can reduce fuel consumption at least 10% and 4.5%, respectively. Meanwhile, it shows the proposed EMS could reduce the battery capacity loss by 6.42%∼9.72% with a slight increase in fuel consumption.

Realistic performance evaluation and optimal energy management of a large-scale bifacial photovoltaic system

This paper introduces an optimal energy management and a comprehensive innovative comparison between on-grid monofacial and bifacial solar photovoltaic systems in terms of energy production, reliability, economic feasibility, ecological impact, footprint area, and other performance indicators. This is to satisfy the load demand of Al-Husainya city, Jordan. Realistic measured data are obtained from authoritative companies in Jordan. This includes ambient temperature, solar irradiance, and a utility-scale load demand. The optimal sizing and energy management strategy are achieved using Marine Predators optimization algorithm. This is done by minimizing Loss of Power Supply Probability. The decision variable is the number of photovoltaic modules. The unit investigation shows that the maximum power of the bifacial panel is increased compared with a monofacial module. So, this will highly affect system size and hence reliability, footprint required area, cost and ecological indicators are impacted too. Results show that the minimized values of the objective function of Loss of Power Supply Probability for monofacial and bifacial systems are 0.3534 % and 0.2511 %, respectively. The number of monofacial and bifacial panels are 12,940 and 11,584 respectively. In addition, the area required to install the bifacial array is decreased by 10.4791 % compared with the monofacial array. Finally, four additional optimization algorithms were used to validate and compare with the obtained results using the Marine Predators algorithm. The methodology described in this paper, which adopts the bifacial photovoltaic technology, is recommended and it can be applied anywhere to get highly reliable, feasible, and more environmentally friendly solar energy systems.

Optimization of Hybrid Energy Storage System Sizing With Considering Energy Management Strategy for High-Power Pulsed Load in Aircraft

High-power pulsed load (HPPL) such as airborne laser weapon and radar poses a severe challenge to aircraft electrical power system. Since peak power cannot be satisfied only by generator, hybrid energy storage system (HESS) should be well sized to match their energetic performances versus instantaneous power requirement and improve reliability of electrical power system. To deal with these challenges, a sizing optimization for HESS with considering energy management strategy (EMS) based on virtual-droop control in aircraft is proposed in this paper. By analyzing the spectrum characteristics of HPPL profile, splitting frequency between different types of energy storage devices is specially analyzed and designed to give full play to HESS. HESS weight, loss and parameters in EMS are taken into account in this multi-objective optimization based on Non-dominated Sorting Genetic Algorithms II (NSGA-II). Series of Pareto fronts can be obtained through the optimization process, and the most suitable solutions of different HESS combinations are selected by a decision-making means which combines subjective and objective weighting methods. Finally, optimization results of four different combinations are compared. Simulation results are obtained in MATLAB/Simulink environment to prove the rationality and applicability of the optimal sizing tool. The research has resulted in a solution of HESS and EMS optimization for HPPL used in aircraft.

Collaborative Optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

A Hierarchical Strategy for Restorative Self-Healing of Hydrogen-Penetrated Distribution Systems Considering Energy Sharing via Mobile Resources

The self-healing ability of distribution systems (DSs) against extreme events can be significantly boosted by strategically dispatching mobile emergency resources (MERs). In recent years, the penetration of hydrogen energy in energy systems has increased dramatically. Hydrogen energy and its related infrastructure, such as hydrogen refueling stations (HRSs) and mobile fuel cell vehicles (MFCVs), exhibit great potential for enhancing the self-healing ability of DSs. In this work, a two-layer hierarchical strategy for improving the restorative self-healing ability of hydrogen-penetrated DS (HPDS) with multiple microgrids (MGs) and HRSs is proposed considering their interactions and MERs such as mobile energy storages and MFCVs. In the lower layer, an optimal energy management strategy is presented for MGs considering the coordination between hydrogen and electricity via the electrolyzer and fuel cell in the HRSs. In the upper layer, an energy sharing (ES) strategy is proposed for the distribution system operator (DSO) to dispatch MERs in order to minimize the power imbalance among MGs and reduce the unserved load. Since MERs can be dispatched for many times to swap energy and hydrogen between MGs or HRSs, the concept of the equivalent time network (ETN) is introduced, and based on the ETN, a mixed integer linear programming (MILP) model is innovatively proposed to represent the traveling pattern of MERs and to optimize their ES path among MGs. Finally, the two-layer hierarchical strategy is formulated as MILP models and solved using the commercial solver. Case studies on a modified IEEE 33 bus distribution system (DS) and a regional DS in China verify the effectiveness of the proposed strategies.

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

This paper presents an innovative approach to the design and real-life field implementation of a hierarchical control solution for a residential ESS (energy storage system) for consumers/prosumers. The proposed control solution minimises residential prosumers’ electricity bill. It consists of an offline controller (secondary level) implemented in the cloud, which works in total synergy with a local real-time controller (primary level) at the ESS. The offline controller proposes an optimal day-ahead energy management strategy using the forecasted demand and PV generation; whereas the local real-time controller is responsible for the management strategy in the best suitable way while dealing with the system real-time constraints and forecast uncertainties. Distributed implementation and demonstration of the solution have been performed through a cloud control platform, operated by an aggregator. Moreover, an offline analysis has been carried out to assess the performances of the real-life implementation outputs and verify the effectiveness of the proposed approach. The obtained results show promising performances of the overall system and implementation approach. This will further ease the integration process of consumer/prosumer with the future virtual power plant or local electricity market operator. The main concept has been developed to be implemented within the StoreNet pilot project platform.

Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

This article investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery aging on the fuel economy, drivability capability, and electric range. The HEV is modeled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery aging. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e., 2–5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive aging.

A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG

AbstractThe involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management.

Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

This study aims to improve the fuel economy of extended range electric vehicles (EREVs) and reduce the cumulative battery workload. Energy management strategy (EMS) of EREVs has a significant impact on improving the energy efficiency, prolonging the service life of batteries, and reducing the fuel consumption. To the best knowledge of the authors, most of existing studies are aimed at optimizing fuel economy, but few researches have taken the service life of the battery into account while improving fuel economy. Our study reflects the power fluctuation range of the battery from the perspective of the battery current, and also further analyzes it from the perspective of the battery energy flow. On the premise of meeting the vehicle power requirement, matrix calculation studies are carried out on the transmission ratio and the key parameters of EMS in the cooperative operation mode of hybrid energy storage system (HESS) based on regular EMS and auxiliary power unit (APU) based on equivalent fuel consumption minimum strategy (ECMS). Via Simulink software, the corresponding vehicle EMS model is developed, and the joint simulation platform of AVL Cruise and Simulink is constructed to verify the effectiveness of the proposed EMS. In order to further tap the energy-saving potential of the proposed strategy based on HESS, the multi-island genetic algorithm is adopted. Under WLTP working condition, the global optimization is conducted with the objectives of minimizing the equivalent fuel consumption and the cumulative ampere-hour through the battery when the vehicle adopts HESS and APU & ECMS operate in concert operation mode. The correlation analysis of the optimization variables is performed. The results show that the fuel economy of the optimized operation mode under WLTP condition is increased by 4.49 %, and the cumulative ampere-hour through the battery is reduced by 11.37 %.

Energy Management Strategy Considering Fuel Economy and Life of Fuel Cell for Fuel Cell Electric Vehicles

The optimization of energy management strategy (EMS) for fuel cell electric vehicles (FCEV) is mainly applied to reduce fuel consumption. However, serious power fluctuations affect the durability of the fuel cell, which is still a challenge for the FCEV. In order to optimize the fuel cell’s service life and economy, this paper proposes an EMS scheme based on fuzzy control. In the proposed EMS, the first layer uses the power following strategy to address the output power distribution issue between the battery and the fuel cell. In order to limit the rapid shift in fuel cell power, the second layer employs a fuzzy control method. Considering the variety of constraints, such as fuel cell power fluctuation and hydrogen consumption, the particle swarm optimization algorithm is used to optimize the membership function of the fuzzy controller. The performance and the operating characteristics of the fuel cells are then evaluated using a test bench. Afterward, the proposed EMS is verified by AMEsim joint simulation. The optimized strategy increased the driving range by 4.7% and 5.9% in world light vehicle test cycle (WLTC) and new European driving cycle (NEDC) conditions, respectively. It also reduces the fuel cell degradation by 21.4% and 19.8%.

Adaptive Model-Predictive-Control-Based Real-Time Energy Management of Fuel Cell Hybrid Electric Vehicles

To compete with battery electric vehicles, fuel cell (FC) hybrid electric vehicles (FCHEVs) are required to offer better performance in fuel economy and FC durability. To this end, this article proposes a novel real-time adaptive model predictive control (AMPC)-based energy management strategy (EMS) for FCHEVs to improve their fuel efficiency and mitigate the degradation of their onboard FC hybrid systems. First, a linear parameter-varying (LPV) prediction model of the FC hybrid system that considers the system parameter variation is developed. The model offers sufficient accuracy while enabling the real-time implementation capability of the AMPC. Then, an AMPC strategy is proposed to optimally distribute the load current of the FCHEV between the FC and the battery in real time. In each control interval of the AMPC, the LPV prediction model is updated online to adapt to the variations of the battery state of charge. The constrained optimization problem of the AMPC is then formulated to achieve a desired tradeoff among four performance metrics and is further transformed into a quadratic programming problem, which can be solved in real time. Hardware-in-the-loop tests are performed on a downscaled FC hybrid system with the proposed AMPC-based EMS, a commonly used rule-based EMS, an equivalent consumption minimization strategy, and an improved MPC-based EMS, respectively. Results show that among the four real-time EMSs, the AMPC-based EMS achieves the best performance in reducing hydrogen consumption and FC current fluctuation and the smallest optimality gap with respect to an offline dynamic programming-based optimal EMS.

Optimal Management of an Energy Community with PV and Battery-Energy-Storage Systems

Most of current prosumer-energy-management approaches are focused on economic optimization by self-consumption maximization. Nevertheless, a lack of energy management strategies (EMS) that tackle different interaction possibilities among community-clustered solar plus battery prosumers has been detected. Furthermore, such active prosumer EMS may include participation in ancillary service markets such as automatic frequency restoration reserves (aFRR) through an optimized battery-energy storage-system (BESS) operation, as well as incorporating community-level energy management. In this study, an optimal EMS that includes aggregated aFRR-market participation of five solar plus battery prosumers participating in an energy community (EC), with the aim of reducing total costs of ownership for each individual prosumer is proposed. For its validation, different scenarios have been analyzed. The results show that the proposed EMS allows a levelized cost of energy (LCOE) reduction for all community members with respect to the base-case scenario. Moreover, the most profitable scenario for all prosumers is still the only PV.

An optimal energy management strategy for a photovoltaic/li-ion battery power system for DC microgrid application

The purpose of this paper is to propose an energy management strategy (EMS) based on flatness control method for a standalone hybrid photovoltaic-battery system. The goal of the proposed method is to use non-linear flatness theory to develop an efficient EMS in order to provide a stable DC bus voltage and an optimal power sharing process between the solar array and the battery. The suggested EMS is responsible for balancing the power reference for the PV system and the battery while keeping the DC bus voltage steady and performing at its reference value. In order to maximize the PV’s power, a perturb and observe with a variable step size (VSSP and P&O) based maximum power point tracking (MPPT) method with a DC/DC boost converter was used. In addition, a DC/DC bidirectional converter was developed to control the charging and discharging process of the battery. Moreover, the proposed EMS strategy was verified in a MATLAB®/Simulink-based simulation environment by subjecting it to a variety of scenarios, including those with varying degrees of irradiation and sudden changes in load. The obtained results show that the presented EMS method was able to keep the bus voltage stable despite changes in load or solar radiation. Furthermore, the EMS By minimizing bus voltage spikes, the technique also ensured excellent power quality which helped the battery’s operation in terms of lifetime and efficiency. Finally, the suggested strategy has a minimum overshoot rate in the bus voltage and higher tracking efficiency compared with the classical load following (LF) strategy under various load conditions.

Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

Adaptation and optimization of a photovoltaic system for a country house

This chapter provides the rationale for monitoring photovoltaic (photovoltaic) systems, its purpose, the need for proper measurement, and the frequency required to obtain meaningful results. The need for system monitoring falls into three groups: user feedback, performance review, and system evaluation. Each group is briefly summarized; other related topics include calibration, data storage, and data transfer. The text covers various monitoring modes such as performance testing, system evaluation, data collection and storage, as well as data analysis and reporting. The evaluation standards given by the International Electrotechnical Commission (IEC) are noted in the Data Analysis and Reporting section. The figures are provided to illustrate several types of displays that are an integral part of monitoring. Optimized modes of operation of the power part of a household photovoltaic system consisting of photovoltaic panels, an MRRT controller, an inverter and an electric energy storage unit, with Autonomous and network connection of the load. To build an optimal and cost-effective energy management strategy, it is necessary to take into account the energy profile of the resident, the characteristics of electricity production based on photovoltaic cells, and the electricity tariff for utilities. A photovoltaic system can be connected to the grid in order to receive energy from the grid or enter it into the network to eliminate the discrepancy between the generated, consumed and stored energy.

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

This paper presents a comprehensive literature review of the state-of-the art modeling and optimization methods for the power and propulsion systems of ships. Modeling is a tool to investigate the performance of actual systems by running simulations in the virtual world. There are two main approaches in modeling: physics-based and data-driven, which are both covered in detail in this survey paper. The output from the simulations might not be optimal in terms of certain performance criteria such as energy consumption, fuel cost etc. Hence, it is vital to optimize the systems considering the efficient interaction between the components, to yield the optimal performance for the integrated vessel’s powertrain. In this paper, the optimization case studies, for the ship energy systems, will be divided in terms of a) optimal design (topology and sizing), b) optimal control and energy management strategies, c) combined optimal design and control. Tables that summarize the literature review outcomes will also be presented at the end of each section. The main outcome is that limited literature is available for optimizations of ship powertrains using data-driven models, especially surrogate models. Surrogate-assisted optimizations for integrated ship energy systems can yield optimal solutions at fast computational speeds, with sufficient accuracy, even for complex, nested, multi-level, multi-objective optimizations.

An Energy Management System for the Optimal Operation of BESS in DC Microgrids: A Robust Convex Programming Approach

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US$ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US$ 5243.23 (19.79%), US$ 5072.23 (15.88%), and US$ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.