Uncertainty Of Renewable Energy Resources Research Articles

Optimal Economic Dispatch and Power Generation for Microgrid Using Novel Lagrange Multipliers-Based Method With HIL Verification

Microgrid incorporating distributed renewable energy resources (RERs) is increasingly important owing to the goal to reach net-zero emissions by mid-century. This article deals with the optimal energy management of microgrid comprising RERs and battery energy storage system. In addition to considering power flow constraints and uncertainties of RERs, the importance of retaining profits of microgrid operators and the needs for providing extra supports to the main grid are rising. To meet all the requirements, a novel Lagrange multipliers-based method is proposed to deal with equality and inequality constraints simultaneously to directly obtain the optimal economic dispatch solution analytically. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the Lagrange multipliers-based method. Furthermore, the comparison of the proposed method with experience-based energy management system, Newton-particle swarm optimization, and deep Q-learning network is provided to evaluate the obtained solutions. Finally, through the hardware in the loop mechanism, which is built using OPAL-RT real-time simulator with floating-point digital signal processor, the effectiveness of proposed Lagrange multipliers-based method is verified and proven to be pragmatic.

A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions

Two of the most critical issues encountered in the day-ahead scheduling of regional integrated energy systems are the uncertainty of renewable energy resources and complexity of load demand. Furthermore, varying operating conditions also pose challenges for economical day-ahead scheduling. This paper proposes a scenario-based day-ahead scheduling approach for regional integrated energy systems to minimize operating costs by mining historical data. A hybrid dynamic energy hub model with variable efficiency that integrates an extreme gradient boosting (XGBoost) algorithm and analytical formulation is proposed. We developed a scenario-based scheduling optimization model in which climate data and load data are predicted using XGBoost and the probability distributions of their predicted errors are estimated using a Gaussian mixture model. Monte Carlo simulation and K-means clustering were used to generate and reduce scenarios and a success-history-based adaptive differential evolution algorithm was adopted to search for optimal solutions for day-ahead scheduling. Furthermore, a weighted average electricity purchasing strategy was adopted to address uncertainty and further improve operating economy by adjusting the output of gas turbines and electricity purchasing for actual scheduling. Case studies were conducted to verify that the proposed approach can reduce daily operating costs and enhance the operating economy of regional integrated energy systems.

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF.

A Hybrid Stochastic/Robust Planning Model for Integrated Energy System Considering Multiple Uncertainties

Abstract Complex energy flows exist in integrated energy systems (IESs), and the optimal assignment of different energy sources has become a primary challenge in the initial stage of its construction. In addition, multiple uncertainties, such as wind power fluctuations and multiple energy load fluctuations, are not well-respected in IES planning. In order to solve the above problems, the generation expansion planning model in IES is proposed. In the model the uncertainties of renewable energy resources (RESs) are described by scenarios, while the power price and fuel price are described by intervals, and a stochastic/robust (S/R) optimization is established to minimize the sum of annual investment cost, operation cost and reliability cost. Based on duality theory, the proposed multi-level S/R optimization model is transformed into a mixed integer linear programming (MILP) model, which can be solved using the CPLEX solver. The proposed model is tested on the IEEE 33-node distribution system with an 11-node cold-heat system. The validity and effectiveness of the proposed model are illustrated by case studies.

Contribution to strengthening Bus voltage stability and power exchange balance of a decentralized DC-multi-microgrids: Performance assessment of classical, optimal, and nonlinear controllers for hybridized energy storage systems control

Stability is the primary goal in standalone power systems due to the high penetration and uncertainties of renewable energy resources, so the system reliability is affected, and the hybrid energy storage system (HESS) and diesel generator (DG) compensate for renewables' failures. This study contributes to managing and controlling DC-coupled multi-Microgrids fed by Photovoltaic, HESS, and DG resources and subjected to pulsed loading and uncertain PV outputs, besides battery SOCs and DG fuel constraints. The Dual-loop control structure compares classical Proportional Integral (CPI), Super-Twisting-Sliding-Mode-Control (ST-SMC), and Linear Quadratic Regulator with Integral Action (LQR-I) to evaluate the robustness and control performance of the HESS. For the supercapacitor (SC), a hysteresis current control (HCC) tracks its setpoint currents using a frequency decoupling-based power-split control. Matlab simulations confirmed system reliability by combining controllers with different Key Performance Indicators, where adopting the LQR-I for bus voltage regulation and the ST-SMC for HESS current control outperformed the other combinations and revealed superior system performance, improving Bus voltage regulation, DG current tracking, overall system efficiency, and loading convergence by 67.63(%), 83.63(%), 77.92(%), and 2.72(%), respectively. Instead, the basic CPI enhanced battery and SC current control with 51.55 and 63.94 (%) compared to the winner scenario.

VVO effect improvements by optimal DER Planning in distribution systems with renewables

With recent installations of intermittent renewable energy generations in electric distribution systems, their impacts on classical Volt-VAR optimization (VVO) schemes, especially the energy-saving effects, are becoming one of the major issues. The optimality of VVO schemes is hard to be guaranteed due to voltage fluctuations caused by distributed renewable generations. Distributed energy resources (DERs) could be a solution to provide flexibilities to improve VVO performance if the high cost is lowered while their locations and sizes are optimized. In this paper, an optimization model is proposed for a joint planning of capacitors, dispatchable distributed generators, and energy storage to improve VVO energy-saving effects while minimizing the system operating cost. The uncertainties of renewable energy resources, electricity demand, and electricity prices are accurately modeled benefited from emerging data analysis technologies. The technical and economic effects of the proposed model are proved and tested using real data sets and compared with classical models. The high precision of the proposed model is verified using time-series simulations.

Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory

In this paper, a bi-level energy management framework based on Conditional Value at Risk (CVaR) and game theory is presented in the context of different ownership of multiple microgrid systems (MMGS) and microgrid aggregators (MAs). The energy interaction between MMGS and MAs can be regarded as a master–slave game, where microgrid aggregators as the leaders set the differentiated tariff for each MG to maximize its benefits, and MMGS as the follower responds to the tariff decision specified by the leader through peer-to-peer (P2P) energy sharing. The P2P energy sharing of MMGS can be regarded as a co-operative game, employing asymmetric Nash bargaining theory to allocate the co-operative surplus. The Conditional Value at Risk model was used to characterize the expected losses by microgrid aggregators due to the uncertainties of renewable energy resources. The Karush–Kuhn–Tucker conditions, Big-M method, and strong duality theory were employed to transform the bi-level nonlinear model of energy management into a single-level mixed integer linear programming model. The simulation results show that when MGs adopt the P2P energy-sharing operation mode, the total operating cost of MMGS can be reduced by 7.82%. The simulation results show that the proposed co-operative optimization framework can make the multiple microgrid systems obtain extra benefits and improve the risk resistance of microgrid aggregators.

Optimal clustering-based operation of smart railway stations considering uncertainties of renewable energy sources and regenerative braking energies

The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers’ uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation.

Techno-economic comparison of different hybrid energy storage systems for off-grid renewable energy applications based on a novel probabilistic reliability index

The application of energy storage technologies is crucial to the extensive exploitation of renewable energy for power generation in off-grid areas because energy storage can mitigate the intermittency of renewables and balance the supply–demand mismatch. A hybrid energy storage system (HESS) with two or more heterogenous and supplementary energy storages outperforms single energy storage from the perspective of reliability and cost-effectiveness, but how to select the optimal HESS configuration is still unknown. To this end, this paper investigates the techno-economic comparison of ten HESSs in off-grid renewable energy system applications, including all pairwise combinations of thermal energy storage, pumped hydro storage, hydrogen storage, battery, and supercapacitor. Moreover, a novel probabilistic reliability index called loss of power supply probability with probability of exceedance (LPSP-PoE) is proposed to quantitatively handle the long-term uncertainties of renewable energy resources. The rule-based coordinated energy management strategies considering the charging/discharging priority and optimized operating thresholds are established for different HESSs, and then the multi-objective optimization models aiming to minimize net present cost and LPSP-PoE are developed to search the optimal components sizing and operating thresholds simultaneously. Finally, the results of case studies show that: (1) the optimal net present cost of thermal energy storage-battery at the highest reliability level is 3.3472 billion USD, which is 6.98 %∼69.85 % lower than the figure for other HESSs, indicating that thermal energy storage-battery is the most cost-effective HESS configuration; (2) The optimal charging/discharging priority can lead to 29.6 %∼75.2 % cost reduction, and the optimal operating thresholds can always achieve the lowest LPSP, illustrating the superiority of the proposed operation strategies; (3) the techno-economic performance of all HESSs varies significantly with the PoE level and load profile, while their rankings remain basically unchanged, showing the robustness of the comparison results.

Adopting Scenario-Based approach to solve optimal reactive power Dispatch problem with integration of wind and solar energy using improved Marine predator algorithm

The penetration of renewable energy resources into electric power networks has been increased considerably to reduce the dependence of conventional energy resources, reducing the generation cost and greenhouse emissions. The wind and photovoltaic (PV) based systems are the most applied technologies in electrical systems compared to other technologies of renewable energy resources. However, there are some complications and challenges to incorporating these resources due to their stochastic nature, intermittency, and variability of output powers. Therefore, solving the optimal reactive power dispatch (ORPD) problem with considering the uncertainties of renewable energy resources is a challenging task. Application of the Marine Predators Algorithm (MPA) for solving complex multimodal and non-linear problems such as ORPD under system uncertainties may cause entrapment into local optima and suffer from stagnation. The aim of this paper is to solve the ORPD problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA is based on enhancing the exploitation phase of the conventional MPA. The proposed enhancement is based on updating the locations of the populations in spiral orientation around the sorted populations in the first iteration process, while in the final stage, the locations of the populations are updated their locations in adaptive steps closed to the best population only. The scenario-based approach is utilized for uncertainties representation where a set of scenarios are generated with the combination of uncertainties the load demands and power of the renewable resources. The proposed algorithm is validated and tested on the IEEE 30-bus system as well as the captured results are compared with those outcomes from the state-of-the-art algorithms. A computational study shows the superiority of the proposed algorithm over the other reported algorithms.

An Auction-Based Local Market Clearing for Energy Management in a Virtual Power Plant

Virtual power plant (VPP) concept creates additional benefits for different stakeholders, such as system operators, distributed energy resources’ owners, and aggregators. This article presents an auction-based energy management solution from VPP aggregator point of view. To this end, a network-constrained economic dispatch has been developed taking into account a comprehensive resource portfolio, including the conventional and renewable distributed generations, energy storage units, and flexible loads. The VPP aggregator aims at minimizing its expected costs through optimal dispatch of its own resources as well as purchasing and selling electricity from/to upstream network. To this end, a stochastic programming approach has been employed to consider the generation uncertainty of renewable energy resources as well as day-ahead energy market prices using a set of plausible scenarios. The payments to the prosumers are according to an auction-based pricing method that could be an efficient strategy for the VPP aggregator in order to guarantee fairness among consumers and producers within the VPP. In the auction-based pricing scheme, the electricity price at each local node can be calculated according to the demand and generation in each time slot. The proposed framework is a mixed-integer linear programming problem and solved by the CPLEX solver in the GAMS software. It is noteworthy that the effectiveness of the model has been verified, considering several numerical studies.

Spinning reserve stochastic model of compressed air energy storage in day-ahead joint energy and reserve market using information gap decision theory method

Regarding numerous benefits of the compressed air energy storage (CAES) in the utility level, these devices can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources and demands. This paper proposes a two-stage mathematical optimization model for optimally day ahead operation of generation units as well as CAESs in energy and reserve markets in a stochastic way. The features of presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the reserve of CAES for providing capability at each hour by six operation modes; (b) considering the limitations related to the state of charge in the CAESs. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in a power system. This paper presents a stochastic optimal operation model on the basis of information gap decision theory together with risk averse strategy in order to overcome this information gap and to help independent system operator. As demand response program, the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed-integer nonlinear problem, which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system demonstrates the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours, lessens the total reserve by 22.75 MW and increases the operation cost by $438.26.

Resiliency-oriented dynamic expansion planning of MG-based distribution networks

High-Impact Low-Probability (HILP) events are the ever-increasing threats against distribution networks (DNs). Boosting DN resilience can be achieved by such measures as redundancy increment by substation allocation, feeder routing from less affected area, design of reconfigurable multi-MicroGrid based (MGs) DN and deployment of different distributed generation sources (DGs). This paper proposes an approach for designing resilient DNs by posing the problem as a dynamic modeling of bi-level resilient DN expansion planning (RDEP) program. In the upper level of the RDEP, distribution company (DisCo) identifies the optimal eco-reliable planning and operation of different assets, while in the lower level, DisCo determines the optimal operation point of the expanded DN against the specified HILP. The uncertainties of renewable energy resources, electric load and its market price and ZIP modeling, well incorporate into the problem. To evaluate the effectiveness of RDEP, computer simulation is done on a large scale 138-bus DN and the results are discussed.

Day-ahead robust optimal dispatch of integrated energy station considering battery exchange service

In order to improve the energy utilization efficiency and decrease the environmental pollution, multiple energy production, conversion and storage devices are integrated as a whole, called an integrated energy station (IES), which can meet various energy demands. In this paper, a day-ahead robust optimal scheduling strategy of IES for electricity, cold energy and heat energy supply is proposed, where the battery exchange service is incorporated by introducing the electric vehicle exchange station (EVES). Distinguished from the prevalent detailed modeling of each battery, an integrated model of EVES based on the state of charge (SOC) interval of batteries is established to reduce the number of variables, by which the charging priority of batteries is embedded into the optimal dispatch of IES. Considering the uncertainties of renewable energy resources and load demands, a two-stage robust optimization model of day-ahead dispatch of IES is formulated to minimize the total operation cost under the worst case, which can be solved efficiently by the column-and-constraint generation (C&CG) algorithm. The effectiveness of the proposed method is demonstrated in the case study. The results show that the total operation cost of IES decreases by 9.2%, indicating the role played by the flexibility of EVES. Moreover, the worst-case cost is reduced compared with the deterministic approach.

Optimal probabilistic reliability-oriented planning of islanded microgrids considering hydrogen-based storage systems, hydrogen vehicles, and electric vehicles under various climatic conditions

Much attention has been paid to the deployment of Hydrogen storage systems (HSSs) and Hydrogen vehicles (HVs) in the modernized energy system. However, a research gap exists in the literature about optimal probabilistic planning of microgrids (MGs) equipped with HSS, considering the uncertainties of renewable energy resources and electric vehicle (EV) and HV owners' behaviors. The main purpose of this research is to fill such a gap by developing a new probabilistic optimization problem to determine the capacity of Hydrogen-based MGs' sub-systems. Another contribution is to consider the reliability constraints and loss of energy cost (LOEC) in the MGs' total net present cost (TNPC). The Monte Carlo simulation (MCS) and Flower Pollination Algorithm (FPA) are used to model stochastic behaviors and solve the proposed probabilistic optimization problem. This paper studies different actual climates of Iran based on historical data, while various coordinated/uncoordinated charging modes of EVs and HVs are examined. Test results infer that a significant inaccuracy (more than 4.66% depends on the climate conditions and vehicle scenarios) occurs due to neglecting the uncertainties. The sensitivity analyses imply that the reliability constraints, LOEC, and their interactions might affect the MGs’ optimal design.

Optimal Dispatch of Multi-Energy Integrated Micro-Energy Grid: A Model Predictive Control Method

In order to reduce the impacts caused by large-scale renewable energy resources accessing the utility grid, the micro-energy grid system, as a natural extension of the microgrid in the energy internet era, is proposed and developed to provide a new solution for the optimal utilization of renewable energy resources. In this paper, a multi-energy integrated micro-energy system is proposed which contains wind, PV, bedrock energy storage, magnetic levitation electric refrigeration, solid oxide fuel cell, solar thermal collector, energy storage, and V2G technologies, and detailed models of the energy generation/conversion/storage devices are formulated. Besides this, the uncertainties of renewable energy resources and cold/heat/electricity loads are considered, and the optimal dispatch problem of the micro-energy system is established from day-ahead and real-time time scales based on a model predictive control method. The day-ahead optimal scheduling aims at economic optimization and guides real-time scheduling, and real-time scheduling utilizes rolling optimization and a feedback correction mechanism to effectively correct the deviation of renewable energy generations and loads at a real-time horizon, which improves the optimization control accuracy, follows the day-ahead dispatch plan, and ensures the economics of real-time scheduling at the same time.

Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction

With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.

Reliability and reserve in day ahead joint energy and reserve market stochastic scheduling in presence of compressed air energy storage

Compressed air energy storages (CAESs) have many advantages in the utility level as an emerging technology and thus, they can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources (RERs) and demands. This paper proposes a two-stage mathematical optimization model for optimally day operation of generation units as well as CAESs in energy and reserve market in a stochastic way. The features of the presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the CAES reserve for providing capability at every hour through six operation modes; (b) considering the limitations related to the state of charge (SOC) in the CAESs. Moreover, the generator reliability is not generally considered during scheduling thermal generation units. Therefore, a model to take into account generator reliability in the scheduling problem is presented by this paper. Regarding the stochastic behavior of some variables in the power system such as demands and RERs, this paper presents a stochastic optimal operation model on the basis of information gap decision theory (IGDT) together with risk averse (RA) strategy in order to overcome this information gap and to help independent system operator (ISO). As demand response program (DRP), the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed integer nonlinear problem (MINLP), which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system shows the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours lessens the total reserve by 21.34 MW and increases the operation cost by $434.54. Moreover, considering the reliability index rises total operation costs by almost 10%.

Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties

Facing a significant increase in connected distributed energy systems, the optimal coordination strategy among multiple distributed energy systems is vitally important to explore. In this paper, an energy management system is introduced and operated by an energy service company, which is responsible for managing the interaction of multiple distributed energy systems. To optimize the day-ahead scheduling of the distributed energy systems, a coordination scheme with a bilevel framework is proposed. The energy interaction between the energy management system and distributed energy systems contains electricity and heat, which is a Stackelberg problem. Two types of internal price schemes, namely, real-time pricing and time-of-use pricing, are discussed. Moreover, the uncertainties of renewable energy resources, energy demand, and energy prices are considered within both upper- and lower-level problems. The problem is formulated as a nonlinear bilevel robust optimization model and transformed into a single-level mixed-integer linear problem. Numerical cases illustrate how the energy management system coordinates with distributed energy systems and show the effectiveness of the coordination strategy such that all participators benefit from the proposed strategy and create a win-win situation. The model and results can serve as references for the business managers of companies that provide energy services for building clusters.

Energy management in microgrids including smart homes: A multi-objective approach

With the penetration of smart homes in distribution systems, and due to the effect of their schedulable load on reducing the peak load of the network as well as their comfort index, microgrid's scheduling in the presence of smart homes has become an important issue. In this regard, this paper presents a tri-objective optimization framework for energy management of microgrids in the presence of smart homes and demand response (DR) program. The model is implemented on an 83-bus distribution system with 11 microgrids. The uncertainties of renewable energy resources (RESs) output power and load demand have been taken into account and the objective function is modeled in the form of bi-objective and tri-objective models using the max-min fuzzy method. The objectives include the operating cost, emissions, and peak-to-average ratio (PAR). The results indicate that an increase in DR penetration reduces the PAR and operating costs and leads to a decrease in the customers’ comfort. Besides, the simulation results show that the best results are obtained from the tri-objective model, and in this model, three goals, including the operating costs, emissions, and PAR index are close to their optimal values, while the customers’ comfort index is also satisfactory. Finally, the results show that considering smart homes in the network reduces the operation cost and emission by about 16 % and 17 %, respectively.