Uncertainty Of Renewable Generation Research Articles

A Health-Aware Energy Storage Sharing Mechanism for a Renewable Energy Base

With the increasing global demand for renewable energy (RE), the growing share of new energy sources has become an inevitable trend. However, due to the uncertainty and fluctuation of renewable energy generation, this poses challenges to the stability of the power system. To mitigate the volatility of wind power output, ensure reliable power supply, and improve energy storage utilization, shared energy storage (SES) can be deployed in renewable energy bases (REBs) to alleviate the pressure on the power supply. However, electrochemical energy storage (EES) faces issues such as lifespan degradation and maintenance cost allocation. In this regard, this paper establishes an EES characterization model considering the dynamic degradation characteristics of batteries and analyzes the coupled relationship between lifespan degradation laws and key parameters in SES operation. Additionally, to assess the impact of electrochemical energy storage’s dynamic degradation characteristics on energy capacity allocation and operational strategies, an optimization model for SES in REBs is developed. Building upon this, a cost allocation mechanism is designed based on the marginal contribution in both the day-ahead and the real-time markets to address the differing demands for SES among different units within the REBs. Case studies are conducted to validate the rationality of the proposed optimization model for SES in REBs and the adaptability of the cost allocation mechanism. The results provide valuable insights for practical applications.

Optimal Operation of Energy Hub: An Integrated Model Combined Distributionally Robust Optimization Method With Stackelberg Game

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

Sensitivity Region-Based Optimization for Maximizing Renewable Generation Hosting Capacity of an Islanded Microgrid

Renewable energy based distributed generators are key components in islanded microgrids. However, their power intermittency and uncertainty may impair power quality and cause system operating constraint violations. It is imperative to evaluate and maximize the hosting capacity of an islanded mi-crogrid for renewable generation. Besides, conventional optimi-zation methods focus on improving the solution robustness on constraints under uncertainties but ignoring that on the optimi-zation objective. To address these unsolved issues, a sensitivity region (SR) based optimization method for maximizing renewa-ble generation hosting capacity of an islanded microgrid is pro-posed. This paper firstly proposes an optimization model consid-ering microgrid frequency variation and microturbine droop control functionality. Secondly, SR and feasibility-SR are adopt-ed to quantify solution robustness against uncertainties of renew-able generation and load. It is expected to enlarge these two re-gions to cover all the possible uncertainty realizations, thus providing robust solutions. Last, this paper develops an SR based optimization method with a new solution algorithm. Through comprehensive numerical simulations, the proposed SR based hosting capacity maximization method is verified with high solu-tion robustness on both objective and operating constraints.

Review of the Impact of Electric Vehicle Charging on Distribution Networks

Abstract: Electric vehicles (EVs) have gained significant popularity, leading to concerns about their impact on distribution networks and the integration of renewable energy sources.In response, researchers have proposed various strategies and methodologies. These include adaptive charging coordination strategies that aim to minimize the influence of EV chargingon distribution networks while considering uncertainties in renewable energy generation and charging demands. Optimizationbased approaches have been employed to balance loads, alleviate network congestion, and coordinate EV charging schedules with renewable energy generation and distribution network demand. The studies have also highlighted potential issues such as distribution system overloading, voltage stability problems, and the need to account for different charging infrastructure scenarios and standards. To address these challenges, control strategies integrating EV charging and battery energy storage systems (BESS) have been proposed. These strategies aim to reduce peak power demands, optimize the utilization of renewable energy sources, and shift charging loads away from peak periods. How- ever, there are still gaps to be addressed, including scalability forlarge-scale deployment, user convenience, and economic viability.Further research is needed to explore these areas and consider the impact of user behavior, smart grid technologies, and cost- effectiveness in accommodating EV charging and plug-in hybrid electric vehicles (PHEVs) in residential distribution grids

An Optimal Scenario-Based Scheduling Method for an SOP-included Active Distribution Network Considering Uncertainty of Load and Renewable Generations

An Optimal Scenario-Based Scheduling Method for an SOP-included Active Distribution Network Considering Uncertainty of Load and Renewable Generations

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

Stochastic restoration of distribution networks with high penetration of solar PV systems through a generic switching order mechanism

The process of fault isolation and service restoration (FISR) is an essential component of the implementation of distribution automation. By using FISR to determine the switching order, whether manually or remotely, the network's reliability is significantly enhanced in comparison to that of conventional switching. On the other hand, it is impossible to refute the exponential development of distributed energy resources such as photovoltaic (PV) generators. This paper introduces a general FISR for distribution networks with high penetration of solar PV systems. The proposed FISR strategy is an optimum switching sequence with the goal of reaching the best restoration hour before any likely fault occurrence. The proposed framework takes into account the inherent uncertainty in network demand and renewable generation in addition to the innovative mathematical programming model for the switching order. In addition, this stochastic programming handles the radiality constraint in the restoration process by creating a fictional layer as a means of reducing the complexity of the modeling. Moreover, the impact of PV penetration level on the optimal switching order for heavy-loaded distribution feeders is addressed and investigated.

Deep Reinforcement Learning Based Real-Time Renewable Energy Bidding With Battery Control

Recently, various renewable energy sources and large-scale batteries have been integrated into power grids, and renewable energy bidding and battery control become critical problems in the real-time energy market. However, bidding and control problems have been studied separately while these two problems simultaneously influence the total profit of renewable producers. In this paper, we propose a novel strategy where renewable energy bidding and battery control are collectively investigated. First, unlike the previous studies where bidding is simply the forecasted value, the proposed methods determine the bidding values considering the error compensability of the battery by switching the objective of forecasting from reducing errors to making errors compensable. After the error compensation, additional battery control is applied to utilize the energy arbitrage process considering the energy price. As there are energy price and renewable generation uncertainties, we propose a deep reinforcement learning based bidding combined with control, called DeepBid, for sequential decision making under uncertainty. Our extensive simulations with real solar and wind generation data show that the proposed DeepBid strategy substantially increases the total profit compared to existing bidding strategies by achieving as high revenues as the arbitrage strategy and as low deviation penalties as the error compensation strategy.

Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty

Integrated energy systems (IES) with renewable energy systems (RES), carbon capture systems (CCS) and energy storage systems (ESS) are considered efficient in supporting the low-carbon energy supply with both economic and environmental benefits. Effective energy management is required to ensure the economical, environmental and reliable operation of the IES. However, the optimal IES operation is considered a non-trivial task due to the renewable generation uncertainty and the optimization of multiple contradictory objectives (e.g. economic, environmental and risk costs). This paper aims to provide a multi-level optimization model for the real-time optimal IES operation consisting of RES, ESS and CCS. This work quantifies the uncertainty by the Conditional Value at Risk (CVaR) theory in the optimization model. The uncertainty is further reduced by improving the operation strategy through a model predictive control (MPC)-based method. Also, the multi-objective optimization model is adopted to minimize the economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling in the intra-day stage. Based on the result of the intra-day stage, the feedback correction model is applied to adjust the schedule to balance the difference between the forecasting and actual values. Numerical results show that the proposed solution can provide the trade-off between economical and environmental performance. Through ablation experiments, the proposed method with feedback correction can carry out demand response with lower costs, CDE and PEC. The proposed solution is further confirmed with outperformed performance compared with single-objective optimization methods and other stochastic optimization methods. In addition, a robustness analysis is conducted to quantify the benefits of RES, ESS and CCS in IES.

Robust optimal scheduling for integrated energy systems based on multi-objective confidence gap decision theory

The multiple uncertainties in renewable energy generation and load pose challenges for the operation of integrated energy systems (IES) with robustness. To achieve robust scheduling of IES, this paper proposes a multi-objective bi-level confidence gap decision theory robust model (MOCGDT). First, a neural network-based surrogate model is used to obtain the inverse cumulative distribution function of a Gaussian mixture model for uncertain parameters, constructing a confidence interval uncertainty set. Then, the upper-level optimization objective is to maximize the fluctuations of wind, solar, and load, while the lower-level objective is to minimize the total operating cost, resulting in an IES MOCGDT robust model. Finally, an effective bi-level solution algorithm is developed for the model's characteristics. The case study results demonstrate that the proposed method improves economic costs by 20.69% and 3.87% compared to robust optimization and stochastic optimization, respectively, and reduces computation time by approximately 601 s compared to stochastic optimization. This suggests that the proposed MOCGDT model not only reduces the conservatism of conventional robust decision-making but also overcomes the coarseness of uncertain sets in the information gap decision theory model, achieving more reasonable robust scheduling with uncertainty. The effectiveness and superiority of the proposed method are thus verified.

Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very time-consuming, and is also not able to provide a level of cost assurance, the deterministic method is not able to determine the effect of uncertainties on system planning, but the IGDT can easily determine the cost of the system based on changes in uncertainty parameters. Also, planning based on the IGDT has resulted in the achievement of a robust hybrid system against forecasting errors caused by uncertainties.

Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power-to-hydrogen devices

To realize renewable and self-sustainable energy supply in island region, based on geographical characteristics with abundant renewable resources, an optimal model for island micro energy grid (MEG) is designed incorporating biomass waste energy conversion system (ECS), desalination, and power-to-hydrogen (BSP-MEG) Firstly, the mathematical model is designed, including models of power generators, ECS, desalination and power-to-hydrogen (P2H) devices, etc. Next, the multi-objective scheduling optimization model is designed, containing conventional scheduling model (Scheduling optimization objectives and constraints established with minimum operation and environment costs) and stochastic scheduling model (Minimum Conditional Value-at-Risk objective specific to volatility and uncertainty of renewable generations based on robust stochastic optimization method). Then, to solve the multi-objective optimization problem (MOP), a hybrid differential evolution algorithm is proposed based on local optimal and external archiving strategies. Finally, the MEG of YongXing Island is selected as an example. The results show (1) BSP-MEG effectively realized multi-energy cooperative optimization, and promote intra-day peak shaving. (2) BSP-MEG reduced operating costs, environmental costs and Conditional Value-at-Risk (CVaR) by 78.2%, 61.8% and 77.9% respectively, while curtailment rate by 25.6 to 0.9%. (3) Whether in general scenario or worst, BSP-MEG can realize self-production and self-sale of energy and material, of which risk resistance ability is better. (4) By designing local optimal and external archiving strategies, hybrid differential evolution algorithm performs better in solving complex MOP. In general, the optimization model proposed in this paper can improve the utilization of renewable resources, alleviate the shortage of fresh water, and help realize renewable and sustainable energy supply.

Three-stage coordinated operation of steel plant-based multi-energy microgrids considering carbon reduction

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods.

Proactive Defense Strategy Against Uncertainty Induced Voltage Disturbance Propagation in Deregulated Market Oriented Greener Grids

Renewable resource proliferation results in clusters with reactive power interdependence within emerging deregulated power grids. The clusters generally form as balancing areas (BA) and the reactive power interdependence is endured through voltage-controlled areas. In such grids, renewable generation uncertainty could manifest as propagating voltage disturbances towards the relatively weaker clusters. Operational and topological constraints of locally optimized conventional reactive support systems limit their capability in alleviating the propagation. This work demonstrates voltage disturbance propagation between BAs and proposes a framework to defend and mitigate them. The framework comprises a proactive resource procurement followed by a proactive first line of defense and a two-stage mitigation strategy. The strategy intelligently utilizes distributed resources through wide area supplementary control. The strategy avails the cyber-physical features of the grid to formulate the multi-criterion decision logic and associated defense as well as mitigation plan. This complex process is realized by an analytical hierarchical process (AHP). Extensive case studies conducted on modified IEEE 68 bus system illustrates the vitality of this decision logic and validates the effectiveness of defense plan and real-time mitigation strategy. The proposed approach also leads to better utilization of available reactive resources and facilitates improved participation in the deregulated market.

Optimal Co-Planning of ESSs and Line Reinforcement Considering the Dispatchability of Active Distribution Networks

The paper presents a method for the co-optimization of energy storage systems allocation and line reinforcement in active distribution networks. The objective is to guarantee the capability of an active distribution network to follow a dispatch plan by appropriately coping with the high uncertainties of loads and stochastic renewable generation while ensuring the secure operation of the grid and minimizing the power grid losses. The proposed formulation relies on a modified formulation of the so-called Augmented Relaxed Optimal Power Flow (AR-OPF) method, which considers the exact (<i>i.e.</i>, non-approximated) AC-OPF in a convexified way (the AR-OPF is proven to provide the global optimal and exact solution of the OPF problem for radial power grids). To tackle the complexity and computational burden of the proposed planning problem, the Benders decomposition algorithm is used and, in order to enhance the convergence speed of the numerical solution of the proposed problem, the Benders decomposition has been suitably modified to determine the energy storage systems site and size sequentially. To assess the performance of the proposed method, simulations are conducted on a real Swiss distribution network composed of 55 nodes and hosting a large amount of stochastic photovoltaic generation. The sensitivity analysis with respect to the photovoltaic capacity is also carried out to assess the effectiveness of the proposed method.

Reinforcement Learning-Based Energy Trading and Management of Regional Interconnected Microgrids

In this paper, we present a Value-Decomposition Deep Deterministic Policy Gradients (V3DPG) based Reinforcement Learning (RL) method for energy trading and management of regional interconnected microgrids (MGs). In practice, the state of an MG is time-varying and the traded energy flows continuously, which is generally neglected in researches. To address this problem, an Actor-Critic framework is adopted. Each MG has to make energy trading decision based on local observation and has no access to any knowledge of other MGs. We bring in the idea of value-decomposition in the training process to ensure the generation of feasible cooperative policies while maintaining MGs’ privacy and autonomous decision-making ability. Furthermore, in light of the uncertainty and fluctuation of renewable energy generation and users’ demand, a recurrent neural network (RNN) with Burn-In initialization is combined with critic network to achieve implicit predictions. Meanwhile, we also take Energy Storage System (ESS) with operational constraints into consideration and deem it as a virtual market innovatively. Experiments have been carried out under real-world data to verify the merit of the proposed method, compared to existing RL-based works.

Optimal Placement of Distributed Generation Units for Microgrid Planning in Distribution Networks

Due to increasing penetration of renewable distributed generation (DG), conventional distribution networks have been gradually transforming into their active form, where microgrids may serve as fundamental building blocks. As the primary step towards microgrid planning, optimal DGs placement and sizing can reduce the total energy losses by localizing power supply to loads. In this paper, a DG optimal placement method by minimizing the total energy losses is proposed, where the planning is formulated as a non-linear programming (NLP) problem. AC optimal power flow (OPF) is used to solve this planning problem by considering operational constraints and uncertainties in load and renewable power generation of the network. IEEE 33-bus test system and a real 404-bus distribution system operated by Saskatoon Light and Power in Saskatoon, Canada are used to validate the proposed method. The proposed method also shows superior performance compared to existing methods.

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG

The popularization of combined cooling, heating and power microgrid containing renewable energy sources (RES-CCHP MG) can effectively alleviate the energy crisis and reduce the emission of air pollutants. However, uncertainties in renewable energy generation and load negatively affect the operation of microgrid. To solve this problem, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. First, the price-based demand response is introduced to establish the RES-CCHP MG model that is more in line with practical application requirements. In the day-ahead dispatching stage, robust adjustment coefficients are introduced to improve the defects of traditional robust optimization which is conservative, and a two-layer robust optimization model is established to improve the ability to resist risks. In the intra-day dispatching stage, in order to reduce the impact of day-ahead prediction error and real-time power fluctuation, the hierarchical rolling optimization model based on model prediction control is established to face the situation that different loads have different response speeds. The simulation results show that compared with the traditional optimal dispatching strategy, the operating cost and the peak-to-valley difference of electric load are reduced by 5.4% and 14.7%, respectively, and the utilization of renewable energy is increased by 4.8%.