Uncertainty Of Renewable Energy Sources Research Articles

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Enhancing resilience of distribution systems: Integrating mobile energy storage systems and information gap decision theory for uncertainty management

Power Distribution Systems (PDSs) have seen considerable disruption owing to events and the intrinsic uncertainty associated with renewable energy sources (RES). The fundamental purpose of this project is to identify methods to enhance the resilience of Mobile Energy Storage Systems (MESSs) against unexpected cyber and natural disasters. Information Gap Decision Theory (IGDT) is used to effectively handle the uncertainties linked with RES's outputs. It also applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict important aspects that affect the output of RES. The shift to microgrid mode prioritizes the delivery of critical loads through MESSs, which are crucial amid adverse situations. A risk-averse strategy mitigates risks associated with wind turbine and photovoltaic (PV) outputs, therefore enhancing the resilience of the PDS. An optimization model utilizing Mixed-Integer Linear Programming (MILP) was formulated. The approach's efficacy in reducing uncertainties in RES has been proved by simulations conducted on the IEEE 69 bus and Dakota transport system. Assuming the most severe situation, the Uncertainty Radius (UR) for wind turbines is 0.7385, but for PVs, it is 0.8753, thereby guaranteeing the robustness of the system against power failures and disturbances.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

A multi‐level coordinated scheduling strategy for shared energy storage systems under electricity spot and ancillary service markets

AbstractThis paper proposes a multi‐level coordinated scheduling strategy for shared energy storage systems (SESS) under electricity spot and ancillary service markets to maximize the overall operational profit. At the upper level, an optimal day‐ahead bidding model is formulated to allocate optimal capacities of SESS for engaging in electricity transactions and offering peak shaving reserve over the scheduling horizon. Then, a joint market clearing model is presented to simultaneously optimize awarded capacities of SESS and marginal prices of electricity spot and ancillary service markets at the middle level. Furthermore, the lower level aims to correct the coordinated scheduling strategy for minimizing the intra‐day scheduling cost under the uncertainty of renewable energy sources and load demand. Finally, a linear relaxation method is applied to transform the proposed strategy into a mixed‐integer linear programming problem for enhancing the computation efficiency. Comparative case studies have validated the superior performance of the proposed strategy for better utilization of available storage capacity and arbitrage maximization.

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Real-time energy management strategy of the hydrogen-coupled microgrid based on Informer model prediction results and deep reinforcement learning

Abstract Microgrids (MG) powered by emerging distributed energy resources (DERs) are increasingly pivotal in driving the decarbonization of the power system. Due to the uncertainty of renewable energy sources, electricity generated from wind and solar is typically stored, either in batteries or in the form of high-pressure hydrogen gas within hydrogen storage tanks to meet the refuelling requirements of fuel cell vehicles (FCVs). For FCVs, the substantial operational expenses associated with hydrogen refuelling stations pose a significant barrier to their widespread adoption, with a notable portion of these costs attributed to hydrogen transportation. To address this challenge, this paper employs the Informer model based on a transformer for day-ahead prediction of photovoltaic power generation, as well as load demand forecasting. Subsequently, the Twin-delayed deep deterministic (TD3) policy gradient algorithm is used for real-time energy management of the microgrid. While ensuring that the State of Charge (SOC) remains above 45% daily to meet future hydrogen demands, the MG pursues an optimal strategy to minimize the total daily cost, including electricity procurement, carbon emissions and equipment degradation. Compared to outcomes derived from rule-based approaches, the algorithm introduced in this paper minimizes solar waste and equipment degradation, facilitating hydrogen production during low-cost electricity periods and enhancing economic feasibility.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

A Bi-level optimization for the planning of microgrid with the integration of hydrogen energy storage

Microgrid (MG) integrated with renewable energy sources (RES) has become increasingly popular, especially when the lack of resources and environmental pollution are serious. However, the uncertainty of RES is the major problem when operating MG. The hydrogen energy storage system (HESS) is a prominent solution for the RES uncertainty since it can store excess energy, supply when in shortage, help to regulate power consumption, and improve MG's flexibility. In this research, a bi-level optimization model for planning the microgrid is proposed. The upper layer of the optimization model aims to minimize the total operation cost of the MG, while the lower level focuses on minimizing the operation cost of the hydrogen energy storage system. The results demonstrate that the proposed bi-level model can help to reduce the operation cost of MG by 42.75% in comparison with typical single level optimization model. Additionally, the electricity generated by diesel generators has also decreased by 52.43%, resulting in a significant reduction in CO2 emissions and contributing to environmental protection.

Solution to uncertainty of renewable energy sources and peak hour demand in smart grid system

Today's need for environmental sustainability prompted the smart grid to incorporate more renewable energy sources. Reducing the operational costs of power generation and emission dispatch can both be accomplished with the integration of renewable energy sources. A power system is made to manage the fluctuating nature of loads for a brief time, nevertheless. However, new difficulties arise for utilities as a result of the fluctuation and uncertainty associated with renewable sources. Another problem faced by generating plants is network reliability issues arising from the peak hour's power demand in the power system. Application of demand response technology with the help of smart meters connected at consumers' end in smart cities can help to ease the enormous peak-hour pressure on power generating units. But a hybrid power system integrated with a storage system faces the challenge of optimal utilization of the battery under uncertainties caused by intermittent generation of power and dynamic demand. This research work proposes a real-time price based hidden Markov model and an enhanced demand response technique to solve these issues in a hybrid power system. Proposed model is tested on IEEE 30-bus system with/without renewable energy sources and implemented grasshopper optimization algorithm for optimal scheduling of generating units.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative methods.

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales.

Optimal Coordination of Energy Coupling System Considering Uncertainty of Renewable Energy Sources

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

Convex Hull Based Economic Operating Region for Power Grids Considering Uncertainties of Renewable Energy Sources

Convex Hull Based Economic Operating Region for Power Grids Considering Uncertainties of Renewable Energy Sources

Multi-objective cooperative optimization of communication base station and active distribution grid under dual carbon targets

To achieve “carbon peaking” and “carbon neutralization”, access to large-scale 5G communication base stations brings new challenges to the optimal operation of new power systems, but also provides new opportunities for the low-carbon development of distribution networks. This paper develops a method to consider the multi-objective cooperative optimization operation of 5G communication base stations and Active Distribution Network (ADN) and constructs a description model for the operational flexibility of 5G communication base stations. Based on this, a multi-objective cooperative optimization 5G communication base station operating model and active distribution network considering the system operation economy and minimum carbon emissions as the optimization objectives are established. In the above model, by encouraging 5G communication base stations to engage in Demand Response (DR), the Renewable Energy Sources (RES), and 5G communication base stations in ADN are concurrently scheduled, and the uncertainty of RES and communication load is described by using interval optimization method. Finally, the problem is solved by combining the equivalent transformation based on interval analysis and the non-dominated sorting genetic algorithm. The analysis results of the example show that participation in grid-side dispatching through the flexible response capability of 5G communication base stations can enhance the power system’s renewable energy consumption and usage efficiency, resulting in significant low-carbon benefits.

Integration of mobile power-hydrogen storage systems in distribution-level networks: A fuzzy information gap optimization framework

In recent years, renewable energy sources (RESs) have had unquestionably significant benefits. However, maximum penetration of RESs has always been challenging due to their intermittent nature. To address this challenge, this study advances a framework for the flexibility of the distribution network (DN) by employing transportable electrical storages (TESs) and transportable hydrogen storages (THSs). To do so, the TESs are considered to transfer electrical energy among high-penetrated and high-demand points. In addition, curtailed renewable power can be converted to green hydrogen and transferred to hydrogen refueling stations (HRSs) using the THSs. To further flexibility, this work considers an incentive-based demand response (DR) program and vehicle-to-parking lot (V2PL) capable plug-in electric vehicles (PEVs). Moreover, a binary-based method is developed to carry out the trajectory planning of the TESs and THSs and the Floyd algorithm is exploited to minimize traveling and simulation time. Furthermore, the fuzzy information gap decision theory (IGDT) is proposed to enhance the scheduling robustness against the uncertainty of RESs. The obtained results corroborated the positive impact of the TESs and THSs by declining the operation cost by about 4.8 %. Moreover, the proposed uncertainty management model is quite robust against RESs’ uncertainty by achieving about 37.5 % less operation cost.

Reconfiguration of the real distribution network and providing a new resilience index, considering the uncertainty of renewable energy sources and digital protection devices in order to improve the resilience

Electric energy, as the most widely used form of energy, has always captured human attention to itself; however, with the climate change and the increase of natural phenomena, it is now necessary to provide solutions to improve the resilience of the network. Resilience is defined as the capability of the system to maintain an acceptable level of performance against a severe disturbance and return to its original condition in an appropriate period of time, which demonstrates the degree of endurance, vulnerability, and reversibility of an infrastructure system. The occurrence of adverse weather conditions and natural disasters causes extensive losses and power outages in distribution networks; and the number and severity of such incidents have seen an increase in recent years. Therefore, it is of great significance to evaluate the resilience of the network and its reversibility capability in dealing with adverse weather conditions. In this paper, after defining the concept of resilience in distribution networks, its characteristics are determined; and then, modeling common natural events such as floods and storms, and introducing a new index to evaluate resilience in distribution networks with digital directional overcurrent relays (DOCR) and Digital dual-setting directional overcurrent relays (DS-DOCR) in the presence of renewable energy sources (RES), the proposed scheme is presented.

Energy scheduling for microgrids with renewable energy sources considering an adjustable convex hull based uncertainty set

In this paper, a risk-based robust energy scheduling model for microgrids is proposed to address the uncertainty of renewable energy sources (RESs). The proposed model introduces a novel adjustable convex hull based uncertainty set (ACHUS), which is a subset of the maximum uncertainty set, to quantify the uncertainty of RESs. The operational risk of an ACHUS is evaluated by a historical data-based method. This method defines the operational risk as the expected penalty cost of all historical data when the operational safety of the microgrid is guaranteed in the ACHUS. Based on the evaluation of the operational risk, a day-ahead energy scheduling model with the ACHUS for microgrids is formulated as a mixed integer nonlinear programming. By introducing auxiliary variables and constraints, the mixed integer nonlinear programming is converted into a solvable mixed integer linear programming. The effectiveness of the proposed method is validated on the IEEE 6-bus and 118-bus systems. The simulation results show that the proposed method can effectively reduce the conservativeness of uncertainty quantification and the improve the economic efficiency of the microgrid.