Uncertainty Of Renewable Energy Research Articles

A novel framework to assess synthetic inertia & primary frequency response support from energy storage systems

Grid integration of large scale uncertain Renewable Energy (RE) generation reduces the share of conventional generation, and thereby reduces the overall system inertia (SI) and Primary Frequency Response (PFR). This necessitates integration of additional sources of inertia and PFR for rapid generation-demand balancing during inevitable contingencies. Due to high-power density and fast response, Energy storage systems (ESS) are evolving as a potential solution for quick grid balancing. Technical viability of these sources to facilitate synthetic inertia and PFR is well established. However, to ensure inertial and PFR adequacy in operational time frame, the Frequency Response (FR) support from these sources must be integrated in Security Constrained Unit Commitment (SCUC) and dispatch decisions. In this context, the present work proposes a Stochastic SCUC framework to model synthetic inertia and PFR support from ESS. Synthetic inertia and PFR support from fast acting ESS are mapped with post-fault frequency dynamics to handle system frequency stability. Further, a machine learning-based Bayesian inference statistic using Markov Chain Monte Carlo algorithm is used to characterize and represent RE uncertainty in scheduling framework. Case studies are performed on IEEE 6 bus and New England 39 bus test systems under various RE integration scenarios to demonstrate the effectiveness of proposed framework. Simulation results highlight ESS potential to significantly enhance post-fault frequency stability in terms of rate of change of frequency, frequency nadir, nadir time, and steady-state frequency.

Economic dispatch of industrial park considering uncertainty of renewable energy based on a deep reinforcement learning approach

The proportions of renewable generations (RGs) and flexible loads (FLs) are rapidly increasing in the industrial park. However, the randomness of RGs and FLs challenges the economics and complexity of the industrial park dispatch. To take this challenge, this paper proposes an optimal dispatch strategy for the industrial park based on a deep reinforcement learning (DRL) approach. Firstly, RGs are modeled based on random fuzzy theory. Dynamic line rating (DLR) is introduced to improve the power flow capacity of RGs model in transmission lines. Secondly, in dispatch process is modeled as a Markov decision process (MDP) to avoid complicated linearization processes. Thirdly, we propose a variant of the distributed proximal policy optimization (DPPO) algorithm. The distributed agents in the algorithm are simultaneously trained to explore optimal strategies in different environments. The trained agents can complete the dispatch based on the current state of the industrial park. The effectiveness of the proposed method is verified by numerous simulations. Compared with PPO, DDPG, and TD3 algorithms, the training time of the proposed approach is reduced by 64.06%, 47.31% and 46.99%, respectively. In different scenes, the operating cost with the proposed method is reduced by 47.19%, 46.23%, 43.41% and 41.71%.

Random Fuzzy Power Flow Analysis for Power System considering the Uncertainties of Renewable Energy and Load Demands

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE.

A multi-energy inertia-based power support strategy with gas network constraints

An integrated energy system with multiple types of energy can support power shortages caused by the uncertainty of renewable energy. With full consideration of gas network constraints, this paper proposes a multi-energy inertia-based power support strategy. The definition and modelling of gas inertia are given first to demonstrate its ability to mitigate power fluctuations. Since partial utilization of gas inertia can influence overall gas network parameters, the gas network is modelled with an analysis of network dynamic changes. A multi-energy inertia-based power support model and strategy are then proposed for fully using gas-thermal inertia resources in integrated energy systems. The influence of gas network constraints on strategy, economy and power outputs is analyzed. Special circumstances where the gas network can be simplified are introduced. This improves the response speed and application value. The feasibility and effectiveness of the proposed strategy are assessed using a real scenario.

Assessing the Effects of Tradable Green Certificates and Renewable Portfolio Standards through Demand-Side Decision-Making Simulation: A Case of a System Containing Photovoltaic Power

Understanding the effect of a tradable green certificate (TGC) and renewable portfolio standard (RPS) policy mix is of great importance for sustainable renewable-energy development and carbon neutrality, given that the demand side subjects are the responsible subjects under China’s RPSs and studies from the demand-side perspective are relatively limited. To fill this gap, this paper analyzes the coupled relationship between the TGC market and the electricity market as well as the reflexivity of the TGC market. Meanwhile, on the basis of modeling TGC prices and renewable-energy uncertainty, this paper constructs a Markov decision process (MDP) model to simulate the sequential decision-making process of the demand side and further proposes a solution model based on dynamic programming and evolutionary algorithms. The results show that: (1) In addition to policy parameters such as RPS weight, TGC price caps and penalties, a preference for short-term benefits and renewable-energy uncertainty also affect transaction behaviors on the demand side. (2) Increasing RPS weight within an appropriate range can stimulate demand for renewable power and TGC, while excessively low RPS weight will result in accumulation of unsold TGC. In addition, the preference for short-term benefits can stimulate demand for renewable power and curb demand for TGC. (3) An increase in the TGC price cap can stimulate demand for renewable power and restrain demand for TGC, but this phenomenon may not exist when RPS weight is too low or responsible subjects prefer short-term benefits. (4) Setting a penalty of no less than the TGC price cap is of great significance to ensure the operation of the TGC market and RPSs.

Meta-Reinforcement Learning-Based Transferable Scheduling Strategy for Energy Management

In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers’ loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia’s electricity network verify the performance of the transferable scheduling strategy.

Reliable design optimization for industrial hybrid energy systems with uncertain sustainable energy

More and more attention has been paid to the industrial hybrid energy systems which comprise fossil energy and renewable energy for achieving carbon neutrality. Wherein, the uncertainty of renewable energy, fluctuation of energy requirement from process units, and failure rate of energy conversion devices have significant effects on the reliability of industrial hybrid energy systems and thus restrict the application and promotion. How to simultaneously address these issues for realizing the reliable design of industrial hybrid energy systems has been a matter of great concern and challenge. Hence, a systematic reliable design optimization method is presented in this study to tackle these challenges. In this study, the two-stage stochastic programming method is implemented to conduct the uncertain optimization in dealing with the uncertainty of renewable energy and fluctuation of energy requirement, while a mixed stand-by redundancy allocation problem is proposed for simultaneously enhancing the reliability and minimizing the number of stand-by devices. A multi-objective mixed integer non-linear programming framework using reliability and total annual cost as the dual objectives is then formulated and applied to a refinery site for the reliable design optimization of the industrial hybrid energy system. According to the solution results, compared to the minimum total annual cost scheme without stand-by redundancy, the maximum reliability of the industrial hybrid energy system can reach 0.9878 using the stand-by redundancy strategy. As a result, the reliability can be significantly enhanced by 44%, with only a 5% increment of the total annual cost. Moreover, a Pareto frontier under multi-objective optimization is constructed, and the optimization schemes at the Pareto frontier can realize the trade-off between reliability and the total annual cost of the industrial hybrid energy system. Subsequently, a reliability-gain index is proposed to further evaluate the trade-off schemes and thus guide the choice of appropriate design schemes.

Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach

Designing a reliable power-to-X (PtX) process that uses renewable energy and grid electricity as backup power is challenging due to the uncertain nature of renewables and potential limits on grid usage. To address this challenge, this paper proposes a data-driven, reliability-based optimization approach for designing a grid-assisted PtX process and understanding how the uncertainty of time-series renewable profile affects its design and optimal sizing. This approach uses generative adversarial networks (GANs) to capture the complex patterns of renewables in temporal and spatial dimensions, rather than simply treating uncertainty as a probabilistic range as in conventional methods. The optimization considers both the mean and variance of production cost as objectives, while also taking into account the level of grid energy penetration as a probabilistic constraint through the reliability analysis. A power allocation and performance evaluation model is developed and used in conjunction with the optimization to assess uncertainty propagation through the PtX process and evaluate the impact of renewable uncertainty on process performance. A case study involving the production of methanol on Jeju Island in South Korea demonstrates how the mean and variance of production cost and other performance indicators can be balanced in the reliable design of PtX processes under renewable energy uncertainty, providing insights for decision-making in such situations.

Robust co-planning of AC/DC transmission network and energy storage considering uncertainty of renewable energy

This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver’s 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model.

Generation Expansion Planning Considering Discrete Storage Model and Renewable Energy Uncertainty: A Bi-Interval Optimization Approach

Both discrete storage model (DSM) and continuous storage model (CSM) have been used in the power system planning literature. In this article, we conduct a sizing-error analysis for the use of CSM in generation expansion planning (GEP), which shows more reasonable storage sizing decisions are offered by the DSM in comparison to the CSM. However, when the DSM is considered in the context of interval optimization, the discrete status variables in mutually exclusive constraints and the strong temporal coupling in state-of-charge constraints create significant challenges. To tackle this, a tailored interval optimization approach is proposed to consider both DSM and renewable energy uncertainty in GEP. Our approach is proved to cover all worst cases in a given uncertainty set, meanwhile running in an iteration-free manner. Moreover, to reduce the conservativeness of investment decisions, a bi-interval policy is designed to achieve a better tradeoff between investment cost and system security.

A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies

Load frequency control (LFC) is well known for balancing the load demand and frequency for a multi-area power system. Studies have proven that LFC can improve the global performance of multi-area power systems. In recent years, the increasing proportion of renewable energy, integration of EVs, and cyber-attacks have become the main challenges in LFC power systems. Different strategies have been applied in the literature for LFC power systems and the possible impacts of renewable energy, EVs, and cyber-attacks. This survey paper is devoted to the research on directions in LFC multi-area power systems. The mathematical model of recent challenges in LFC multi-area power systems is summarized and the similarities and differences of these challenges are analyzed. The uncertainty of renewable energy is a frequently noted issue in LFC power systems; however, the uncertainty that exists in controller design is often ignored. In this survey, we analyze methods for treating the uncertainty of renewable energy and controller. This survey paper introduces the most recent research on LFC and acquaints anyone interested in its development, such that the most effective strategies can be developed by the researchers.

Combing data-driven and model-driven methods for high proportion renewable energy distribution network reliability evaluation

To accurately evaluate the reliability of the renewable energy distribution network, a combined data-driven and model-driven method of distribution network reliability evaluation are proposed. The approach improves the traditional reliability evaluation method in the uncertainty scenario generation and fault consequence analysis stages. In the scenario generation stage, an interpretable network architecture capable of adaptively learning the inherent randomness, volatility, and time variability properties of renewable energy is designed. The proposed DG output uncertainty generation method integrates the Conditional Wasserstein Generative Adversarial Network (CWGAN) based scenario generation and clustering-based scenario reduction. It accurately portrays uncertainty in distributed generation (DG) output power. In the fault consequence analysis stage, an optimization model for the reliability evaluation of distribution networks based on mixed integer linear programming is proposed. The model fully accounts for substation protection, branch line protection, network reconfiguration, DG, and energy storage system (ESS) components. Time-series analytical expressions for the corresponding reliability indices are established. Finally, with the combination of data-driven and model-driven methods, a distribution network reliability evaluation framework considering the uncertainty of renewable energy is proposed. Simulation tests are performed on a modified IEEE 123 bus system. The results verify the effectiveness and feasibility of the method. The proposed simulation analysis method can effectively quantify the distribution networks’ reliability compared to the traditional method. It has higher calculation accuracy.

Research on Highway Self-Consistent Energy System Planning with Uncertain Wind and Photovoltaic Power Output

Highways are a critical consumer of energy. The integration of the highway and the energy system (ES) is a proven method towards carbon neutrality. The increasing energy demands of highway transportation infrastructure and the development of distributed energy and energy storage technologies drive the coupling between the highway system (HS) and the energy supply network, which is becoming tighter than ever before. Many scholars have explored the mode and path of integrated transportation and energy development. However, the energy and transportation systems’ coupling relationship and the collaborative planning scheme have not been thoroughly studied. Facing the increasing interconnection between transportation and energy networks, as well as addressing the demand for clean energy in highway transportation effectively, this paper proposes a highway self-consistent energy system (HSCES) planning model integrating uncertain wind and photovoltaic (PV) power output, so as to analyze the energy supply mode of the HS and determine the multi-energy capacity configuration of the self-consistent energy system (SCES). Firstly, the mathematical model related to each micro-generator of the SCES and the load aggregation scenario of the HS is established. Secondly, considering the uncertainty of renewable energy, this paper focuses on wind and PV power generation, and abatement technology, under uncertain conditions to ensure the best solution for reliability. Thirdly, taking the economy, reliability and the renewable energy utilization rate of the system into account, the system planning model is established under the condition of ensuring the system correlation constraints. Finally, the proposed method is validated using a section of the highway transportation system in western China. The results show that the hybrid energy storage planning scheme can cause the system’s renewable energy utilization rate to reach 99.61%, and the system’s power supply reliability to reach 99.74%. Therefore, it is necessary to carry out coordinated planning while considering the characteristics of the HS and the ES, which can minimize the planning cost of a HSCES, reduce the waste of wind and solar energy, and ensure the reliability of the power supply for the HS.

Key Technologies and Development Challenges of High-proportion Renewable Energy Power Systems

China promises to reach peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. It will need to make every effort to achieve these objectives. Among all, the development of the renewable industry is the key way and roadmap. When high-proportion wind and solar energy are integrated into the power grid. The randomness and uncertainty of renewable energy reduced the safety margin and influenced the stability of the power grid. In this paper, the present situation of renewable energy in China is introduced. Total installed solar generation capacity was about 250 GW in China by the end of 2020, accounting for one-third of the world's total installed capacity. The capacity for wind energy generation was about 280GW. It has the highest generation capacity in the world. It also presents the potential challenges of integrating large-scale renewable energy into the power system and proposes mitigation techniques and is valuable for further research.

Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations

Cascade hydropower stations are excellent flexible resources to regulate the drastic fluctuations of wind and photovoltaic power generation in the hybrid energy system. By constructing pump stations between two adjacent upstream and downstream reservoirs, the conventional cascade hydropower stations can be transformed into a cascade pumped hydro energy storage (CPHES) system, which further can promote the integration of clean energy resources. In this paper, a two-stage robust unit commitment model for the cascade hydropower stations retrofitted with pump stations is established to address the renewable energy uncertainties. A short-term scheduling framework is proposed for the hybrid energy system including CPHES, which coordinates the operating cost and clean energy curtailment. Besides, a modified nested column-and-constraint algorithm is employed to solve the two-stage robust optimization problem with integer resources. Numerical tests performed on a modified IEEE 24-bus system and a large-scale practical power system verify the effectiveness of the proposed scheduling model and algorithm.

Optimal renewable energy export strategies of islands: Hydrogen or electricity?

Renewable energy has the characteristics of wide distribution, sustainable use and low impact on the environment, which is suitable for application and promotion in the island area. Given the concerns about wind and PV curtailment of energy-rich islands, it is potentially a good idea to export extra renewable energy to the mainland. The aim of this paper is to investigate the economic viability of transforming renewable energy into exportable electricity or hydrogen. A comprehensive renewable energy system model is developed based on the P-graph to simulate an energy system, which integrates electricity, heat and hydrogen on a virtual island. Solar, wind, wave and biomass are the main renewable energy sources in the developed energy system. The objective includes the construction cost, operating cost, and also environmental cost, which is related to the greenhouse gas footprint. Findings from the case study show that extra renewable energy exporting by electricity is cheaper than using hydrogen for the studied island, and the optimal dispatch structure can deliver 51 GWh of electricity annually for 292 M CNY (about 42 M EUR). A sensitivity analysis of export prices and renewable energy uncertainty verifies the economics of electricity export.

Research on Industrial and Commercial User-Side Energy Storage Planning Considering Uncertainty and Multi-Market Joint Operation

With the continuous development of the Energy Internet, the demand for distributed energy storage is increasing. However, industrial and commercial users consume a large amount of electricity and have high requirements for energy quality; therefore, it is necessary to configure distributed energy storage. Based on this, a planning model of industrial and commercial user-side energy storage considering uncertainty and multi-market joint operation is proposed. Firstly, the total cost of the user-side energy storage system in the whole life cycle is taken as the upper-layer objective function, including investment cost, operation, and maintenance cost. The lower layer takes the economy and environment of energy storage operation as the goal, and considers the ancillary service market revenue, demand response constraints, and operational constraints. Secondly, considering the uncertainty of the power market price, and based on the robust optimization theory, the robust transformation is carried out to effectively deal with the impact of uncertain variables on the system operation. Finally, the model is verified in a typical IEEE 30-node system. The results show that the uncertainty of renewable energy will affect the optimal location and capacity of energy storage. From the results of energy storage location, energy storage will be configured in the important transmission nodes and renewable energy power generation access nodes in the power system.

Identifying Economic and Clean Strategies to Provide Electricity in Remote Rural Areas: Main-Grid Extension vs. Distributed Electricity Generation

The policy decision of extending electric power transmission lines to connect a remote area to a primary grid vs. developing local electricity generation resources must be informed by studies considering both alternatives’ economic and environmental outcomes. Such analysis must also consider the uncertainty of several factors such as fuel prices, the cost and performance of renewable and conventional power generation technologies, and the value of environmental benefits. This paper presents a method for this analysis, making two main contributions to the literature. First, it shows how to characterize the two alternatives (i.e., main-grid extension vs. local power generation) in detail for precise quantification of their capital and operating costs while guaranteeing that they are both adequate to meet forecast demand and operating reserves. Second, it shows how to properly account for the economic and environmental implications of renewable energy intermittency and uncertainty through the optimization of capital investments and hourly operations. The method is illustrated by applying this analysis method to Saudi Arabia, where the government is struggling to outline a strategy to meet residential and commercial loads reliably and sustainably in the country’s remote, scattered, isolated areas. To meet this demand, the Saudi government is considering two main alternatives: (1) extending the primary power transmission grid; or (2) installing an optimal combination of off-grid distributed generation (DG) resources, including solar PV, wind, diesel, oil, heavy fuel oil, and Li-ion batteries, to generate the electricity locally. Results suggest that under most scenarios of capital costs, fuel prices, and costs of air pollution, developing a microgrid with a large share of wind and solar power is more cost-effective than extending a primary grid 150 km or more away. Extending a primary grid powered by gas-fired combined-cycle power plants is more economical only if the load is not very high, the distance is not more than 350 km, and oil prices are relatively high compared to natural gas.

Two stage robust planning of park integrated energy system considering low carbon

The development of integrated energy systems is a significant way of improving energy efficiency and encouraging renewable energy absorption. However, the uncertainty of renewable energy and loads raise severe challenges to integrated energy system planning. Besides, under the low carbon background, the relationship between reducing carbon emissions and increasing the absorption of renewable energy is not investigated clearly in the planning of the park integrated energy system. Thus, this paper proposes a two-stage robust planning method considering low carbon and uncertainty. Firstly, the input-output model of energy conversion equipment in the park integrated energy system is described. Secondly, the objective function and constraints and are set up. Specifically, the carbon emission limits and renewable energy penetration target are introduced in the constraints. On this basis, the two-stage robust planning model with min-max-min structure is established, after transformation, column-and-constraint generation is used for the solution. Case study shows the ability and cost of the park integrated energy system to promote low-carbon emission reduction and renewable energy consumption. Moreover, it proves that although there is some synergy between the low carbon target and the renewable energy consumption target, maximum renewable energy consumption does not mean minimum carbon emissions, and vice versa, and in some cases increasing renewable energy penetration does not necessarily reduce carbon emissions.

Peer-to-peer energy trading market considering renewable energy uncertainty and participants’ individual preferences

With the development of renewable energy resources, peer-to-peer (P2P) energy trading is becoming more and more popular on the user side. However, the prosumers’ uncertain fluctuation and specific preference requirements have brought new challenges to the traditional market mechanism. Therefore, we propose a novel pricing mechanism, including preference marginal price (PMP), uncertainty marginal price (UMP), and local marginal price (LMP) in this paper. These pricing strategies can overcome the uncertainty problems in the current market and credit the reserve resources that ensure flexible energy delivery. We embed these pricing strategies into the robust economic dispatch model and employ a column-and-constraints generation (CCG) algorithm to solve the model. Among these prices, local marginal prices are used to charge energy consumption and financial transmission right (FTR) credits. Uncertainty marginal prices are charged for the reserve resources and financial transmission right holders. Preference marginal prices are charged from the prosumers with specific welfare requirements to credit the reserve resources, financial transmission right holders and energy producers. The market equilibrium and revenue adequacy are proven in the appendix to verify the effectiveness of the proposed pricing mechanism. In the simulation section, the total operation costs and marginal price payments increase with the rise of uncertainty level and preference requirement degree. The financial transmission right underfunding problem is verified in a three-bus system. The underfunding can be covered by congestion funds collected from uncertainty and preference marginal prices.