Uncertainty Of Renewable Energy Research Articles

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

Optimizing peak shaving operation in hydro-dominated hybrid power systems with limited distributional information on renewable energy uncertainty

The increasing integration of renewable energy sources (RES) in power systems poses challenges for peak shaving operations due to RES uncertainty. However, it is difficult to obtain complete distributional information for uncertainty modeling. This study focuses on optimizing peak shaving in hydro-dominated hybrid power systems under such uncertainty. We utilize limited distributional information of RES forecast errors, specifically the first two moments, to build a moment ambiguity set. Employing distributionally robust chance-constrained programming (DRCCP), we develop a peak shaving model that quantifies the flexibility reserve of hydropower by risk level and the forecast errors. To enhance computational tractability, we apply the Chebyshev inequality to reformulate the moment-based DRCCP model into a mixed-integer linear programming model. Numerical simulations conducted on a provincial power grid in China validate the model's effectiveness. Key findings indicate that: (1) The model effectively leverages hydropower to provide ramping flexibility for peak shaving and quantifies the flexibility reserve needed for RES forecast errors. (2) This uncertainty modeling approach is more practical than probability distribution function-based methods, ensuring reliable peak shaving scheduling and reducing conservatism. (3) Decision-makers can adjust risk level to modify hydropower flexibility reserve, balancing robustness and conservatism of peak shaving scheduling.

A Review of Reliability Research in Regional Integrated Energy System: Indicator, Modeling, and Assessment Methods

The increasing complexity of integrated energy systems has made reliability assessment a critical challenge. This paper presents a comprehensive review of reliability assessment in Regional Integrated Energy Systems (RIES), focusing on key aspects such as reliability indicators, modeling approaches, and evaluation techniques. This study highlights the role of renewable energy sources and examines the coupling relationships within RIES. Energy hub models and complex network theory are identified as significant in RIES modeling, while probabilistic load flow analysis shows promise in handling renewable energy uncertainties. This paper also explores the potential of machine learning methods and multi-objective optimization approaches in enhancing system reliability. By proposing an integrated assessment framework, this study addresses this research gap in reliability evaluation under high renewable energy penetration scenarios. The findings contribute to the advancement of reliability assessment methodologies for integrated energy systems, supporting the development of more resilient and sustainable energy infrastructures.

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Optimal Flexibility Dispatching of Multi-Pumped Hydro Storage Stations Considering the Uncertainty of Renewable Energy

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study.

A study of electricity sales offer strategies applicable to the participation of multi-energy generators in short- and medium-term markets

Due to the increasing proportion of renewable energy, a multi-layered and multi-timescale energy market has emerged in many countries such as China. In the meanwhile, power generation companies must develop more intelligent and dynamic offer strategies to adapt to today's intricate energy trading. Because of the difficulty in describing the dynamic trading environment caused by the uncertainty of renewable energy, previous studies have not fully explored the offer strategy especially in both short-term and medium-term electricity markets. In response to this challenge, this research introduces a novel biding strategy framework leveraging a Asynchronous Advantage Actor-Critic (A3C) algorithm, which can effectively address the decision making in dynamic and uncertain energy markets. The framework focuses on intra-monthly transaction clearing mechanisms with the aim of optimally enhancing earnings. The research formulates an offer model both for thermal and renewable power generation enterprises, which is applicable to medium-term monthly and intra-monthly trading. The study then validates this framework through three distinct analyses: the returns of various bid methods under standard scenarios, the offer strategies return of power generation companies with diverse cost profiles, and the impact of varying renewable energy proportions. The multi-angle simulations confirm that the model presented in this paper offers a scientific basis for the development of offer strategies for power generation companies and enable power generating firms to effectively adopt to the current power market.

Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy

Hydropower, characterized by its quick ramping speed and energy buffering capacity, has the ability to respond to the flexibility demand caused by the forecast uncertainty of variable renewable energy (VRE). However, how to determine the flexibility supply of each hydropower station to ensure the reliable execution of the day-ahead scheduling in the intraday operation is a challenging issue for a hydro–wind–solar hybrid system (HWSHS). Due to complex characteristics of cascade hydropower stations, there is a mutual influence between the day-ahead scheduling and its execution. Therefore, flexibility response mode (FRM) is proposed to determine the flexibility supply of each hydropower station according to the flexibility demand of VRE, and a co-optimization model is constructed to optimize the day-ahead scheduling and the parameters of FRM considering the forecast uncertainty of VRE. A HWSHS in Guizhou province, China, is employed as a case study. The results suggest that the FRM can ensure the reliable execution of the day-ahead scheduling in the intraday operation. Compared to a contrast model without FRM, the execution deviation and its duration are decreased by more than 22% and 37%, respectively, in both long-run and out-of-sample analyses.

Strategic investments and portfolio management in interdependent low-carbon electricity and natural gas markets

Addressing global warming necessitates carbon emissions reduction and renewable energy integration within the energy sector. Gas-Fired Power Plants (GFPP) are appealing to investors due to their low emissions and operational flexibility, which are considered necessary characteristics within low-carbon power systems with increasing renewable energy uncertainty. Investing in GFPPs presents intricate challenges due to the increasingly interdependent electricity and natural gas markets, especially in the light of a low-carbon economy. This work addresses these challenges for a strategic agent by proposing a bi-level optimization framework. The upper-level model derives the optimal electricity portfolio management regarding new investments and strategic biddings, while in the lower-level model, the electricity and gas markets are cleared sequentially under a Carbon Emission Trading Scheme (CETS). Case studies on a Pennsylvania-New Jersey- Maryland (PJM) 5-bus power system and an IEEE 24-bus test system demonstrate the applicability and efficacy of the proposed model in capturing the impact of a transitional integrated market framework on GFPPs investments. Also, introducing stochasticity to the model provides a better insight into the contrasting effects of emission allowance trading and gas prices on investment and bidding strategies.

Unit commitment of power systems considering system inertia constraints and uncertainties

AbstractLarge‐scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time‐domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed‐integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

Maximization of Total Profit for Hybrid Hydro-Thermal-Wind-Solar Power Systems Considering Pumped Storage, Cascaded Systems, and Renewable Energy Uncertainty in a Real Zone, Vietnam

The study maximizes the total profit of a hybrid power system with cascaded hydropower plants, thermal power plants, pumped storage hydropower plants, and wind and solar power plants over one operation day, considering the uncertainty of wind speed and solar radiation. Wind speed and solar radiation in a specific zone in Vietnam are collected using the wind and solar global atlases, and the maximum data are then supposed to be 120% of the collection for uncertainty consideration. The metaheuristic algorithms, including the original Slime mould algorithm (SMA), Equilibrium optimizer, and improved Slime mould algorithm (ISMA), are implemented for the system. ISMA is a developed version of SMA that cancels old methods and proposes new methods of updating new solutions. In the first stage, the cascaded system with four hydropower plants is optimally operated by simulating two cases: simultaneous optimization and individual optimization. ISMA is better than EO and SMA for the two cases, and the results of ISMA from the simultaneous optimization reach greater energy than individual optimization by 154.8 MW, equivalent to 4.11% of the individual optimization. For the whole system, ISMA can reach a greater total profit than EO and SMA over one operating day by USD 6007.5 and USD 650.5, equivalent to 0.12% and 0.013%. The results indicate that the optimization operation of cascaded hydropower plants and hybrid power systems can reach a huge benefit in electricity sales

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Knowledge mapping and evolutionary analysis of energy storage resource management under renewable energy uncertainty: a bibliometric analysis

The research on energy storage resource management is an important measure to cope with the present problem of uncertainty in the use of renewable energy, in order to explore the evolution of the research focus and future trend of energy storage resource management under the uncertainty of renewable energy, this paper adopts the method of bibliometric analysis, and conducts a visual analysis of the relevant 800 documents in the core collection of the Web of Science by using the CiteSpace to draw co-occurrence maps and analyze the collaborative networks of different countries/regions, institutions and authors in this field, and further conducted journal co-citation analysis, literature co-citation analysis and author co-citation analysis. The keyword co-occurrence, emergent analysis, and cluster co-occurrence analysis reveal the current research focus and trend in this field, and summarize and propose four future key focus directions: energy storage technology improvement, energy storage system integration, expansion of business models for energy storage resource management, and intelligent control of energy storage system, which provide new research paths for solving the problem of renewable energy uncertainty in the future.

Two-Stage Robust Optimization for Large Logistics Parks to Participate in Grid Peak Shaving

As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load’s Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks’ participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park’s net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%.

Probabilistic assessment of short‐term voltage stability under load and wind uncertainty

AbstractContemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (>80%) and dynamic load (>75%), as well as encountering a large disturbance under uncertain circumstances.

Distributionally robust risk-resistant synergistic restoration strategy for integrated electricity and gas system with high-fidelity gas dynamic modeling

A synergistic restoration strategy offers a promising solution for expediting restoration of integrated electricity and gas system (IEGS) following blackouts, particularly under the increasing energy interdependence between these two subsystems. However, achieving synergistic restoration is challenging in 1) distinct operational characteristics between electric and gas flow rates, leading to difficulties in accurately modeling the restoration processes with significantly varying system states; 2) potential risks posed by uncertainties, such as fluctuating renewable energy and random contingencies, undermining the effectiveness of restoration strategy. To address these challenges, this paper proposes a novel distributionally robust risk-resistant synergistic restoration strategy for IEGS. Firstly, to accurately capture the dynamics of slow gas flow relative to instantaneous power flow, we propose a high-fidelity linear dynamic gas flow model with multiparametric disaggregation technique, demonstrating satisfactory accuracy in restoration scenarios. Then, the risks by uncertainties of renewable energy and contingencies are handled within distributionally robust framework. Moreover, the IEGS restoration problem is reformulated as a two-stage robust optimization problem by convex conservative approximation and strong duality theory, which is solved by a nested column-and-constraint generation algorithm. Finally, simulation results validate the effectiveness of the proposed restoration strategy, showing the improved restoration efficiency, security, and risk-resistant performance.

Human-safe and economic operation of renewable hydrogen-based microgrids under plateau conditions

The cold and hypoxic environment threatens the health and quality of people's lives in plateau areas. Appropriate air pressure, oxygen, and temperature are crucial for human-safe aspects in plateau microgrids. Facing extreme scenarios in these areas, we propose a two-stage robust operation strategy for renewable hydrogen-based microgrids considering the reliable supply of hydrogen and oxygen. To cope with rapid weather changes in these areas, the distributionally robust optimization (DRO) model that incorporates first-moment information is employed to depict renewable energy uncertainty. This study focuses on two weakly connected microgrids with distinct characteristics, showcasing their potential for mutual support. In the first stage, the plateau microgrid fulfills demands for electric power, hydrogen, and oxygen, with the power support from the another microgrid. In the second stage, reserves such as hydrogen fuel cells, diesel generators, and hydropower are utilized to mitigate renewable energy uncertainty. The operation optimization problem based on DRO is solved using the alternating direction method of multipliers. Case studies show that the strategy can coordinate complementary operation to enhance reliability, guarantee human safety, and save costs by 39.29 % and 38.73 % for the two microgrids.

Hierarchical deep reinforcement learning for self-adaptive economic dispatch

It is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility of renewable energy. Deep reinforcement learning (DRL) algorithms are introduced as a solution to avoid modeling the complex uncertainties and to adapt the fluctuation of uncertainty by interacting with the environment and using feedback to continuously improve their strategies. However, the large-scale nature and uncertainty of the system lead to the sparse reward problem and high-dimensional space issue in DRL. A hierarchical deep reinforcement learning (HDRL) scheme is designed to decompose the process of solving this problem into two stages, using the reinforcement learning (RL) agent in the global stage and the heuristic algorithm in the local stage to find optimal dispatching decisions for power systems under uncertainty. Simulation studies have shown that the proposed HDRL scheme is efficient in solving power system economic dispatch problems under both deterministic and uncertain scenarios thanks to its adaptation system uncertainty, and coping with the volatility of uncertain factors while significantly improving the speed of online decision-making.

A multi-timescale and chance-constrained energy dispatching strategy of integrated heat-power community with shared hybrid energy storage

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers’ demands, and the multi-timescale scheduling of heat and power. In this paper, we propose a game theoretic model of the integrated heat-power system. For the welfare-maximizing community operator, its energy dispatch strategy is under chance constraints, where the day-ahead scheduling determines the scheduled energy dispatching strategies, and the real-time dispatch considers the adjustment of generators. For utility-maximizing consumers, their demands are sensitive to the preference parameters. Taking into account the uncertainty in both renewable energy and consumer demand, we prove the existence and uniqueness of the Stackelberg game equilibrium and develop a fixed-point algorithm to find the market equilibrium between the community operator and community consumers. Numerical simulations on integrated heat-power community show that the proposed solution outperforms the constant curtailment strategy with the daily cost reduced by 14.38%.

Double-layer optimal scheduling method for solar photovoltaic thermal system based on event-triggered MPC considering battery performance degradation

Solar photovoltaic thermal system (SPTS) can fully tap solar energy resources to realize thermal and electric supply for users simultaneously, but the volatility and uncertainty of renewable energy and load cause the imbalance of energy supply. This paper proposes a multi-time scale optimal scheduling method for SPTS based on event-triggered model predictive control (ET-MPC) considering battery performance deterioration. Firstly, SPTS is divided into day-ahead and day-in optimization layers from different time scales. Day-ahead scheduling takes the minimization economic cost as the optimization function to obtain pre-scheduling plan, and day-in scheduling adapts rolling optimization scheme based on model predictive control (MPC) to reduce power fluctuations and achieve optimal scheduling adjustment. Moreover, the battery performance decay parameter is introduced into the MPC model and embedded into the optimization problem to prolong the cyclic life deterioration, and an event-triggered mechanism is introduced into MPC to improve the computational efficiency and reduce the communication frequency. Finally, the results show that the proposed double-layer scheduling method improves 4.15 %, 66 % and 13.39 % respectively regarding cost, power fluctuation and battery maintenance, and improve the calculation efficiency of 48.89 % compared with standard MPC method, which indicates that the proposed method has good economy, stability and execution efficiency.