Uncertainty Of Renewable Generation Research Articles

Low-carbon economic operation strategy for multi-agent integrated energy system considering uncertainty of renewable energy power generation

The uncertainty of renewable energy output threatens the operation safety of multi-agent integrated energy system (MAIES), which makes it difficult to balance the low-carbon economic operation demands of various stakeholders. However, the existing research solely focuses on the operational strategy of multi-agent game involving integrated energy suppliers and users in deterministic scenarios, overlooking the complementary supporting role and game interaction of shared energy storage and wind farm as independent entities of interest under the instability of renewable energy power generation. Hence, this paper first establishes the optimal operation models for integrated energy system operator (IESO), user aggregator (UA), shared energy storage operator (SESO), and wind farm operator (WFO) considering the stepped carbon trading. Second, in the face of the actual situation of uncertainty of photovoltaic and wind power output, fuzzy chance-constrained programming is adopted for processing. Then, a bi-layer game equilibrium model with IESO as a leader and UA, SESO, and WFO as followers is proposed, and the existence and uniqueness of Stackelberg equilibrium solution are proved. Finally, simulation calculation is carried out based on the YALMIP toolbox in the Matlab R2023a software, and the improved particle swarm optimization algorithm and CPLEX solver are used to solve the model. The results demonstrate that the participation of SESO and WFO as independent stakeholders in the game interaction can improve the economic and environmental benefits of MAIES. The iterative optimization of demand response subsidy prices can effectively motivate users to participate in demand response, improve the ability of MAIES to cope with the uncertain risks of renewable energy generation and load, and reduce the power grid dispatch pressure.

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Bidding strategies for multi-microgrid markets taking into account risk indicators

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.

Stochastic optimization framework for hybridization of existing offshore wind farms with wave energy and floating photovoltaic systems

Due to the complementarity of renewable energy sources, there has been a focus on technology hybridization in recent years. In the area of hybrid offshore power plants, the current research projects mostly focus on the combinational implementation of wind, solar, and wave energy technologies. Accordingly, considering the already existing offshore wind farms, there is the potential for the implementation of hybrid power plants by adding wave energy converters and floating photovoltaics. In this work, a stochastic sizing model is developed for the hybridization of existing offshore wind farms using wave energy converters and floating photovoltaics considering the export cable capacity limitation. The problem is modeled from an investor perspective to maximize the economic profits of the hybridization, while the costs and revenues regarding the existing units and the export cable are excluded. Furthermore, to tackle the uncertainties of renewable energy generation, as well as the energy price, a scenario generation method based on copula theory is proposed to consider the dependency structure between the different random variables. Altogether, the hybridization study is modeled in a mixed integer linear programming optimization framework considering the net present value of the project as the objective function. The results showed that hybrid-sources-based energy generation provided the highest economic profit in the studied cases in the different geographical locations. Furthermore, the technical specifications of the farms have also been considerably improved providing more stable energy generation, guaranteeing a minimum level of power in a high share of the time, and with a better utilization of the capacity of the cable while the curtailment of energy is maintained within the acceptable range.

A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

AbstractThe increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low‐carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer‐to‐peer (P2P) electricity and carbon trading in multi‐energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value‐at‐risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision‐making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

Chance-Constrained optimal scheduling of power and hydrogen systems in power distribution network with significant photovoltaic and carbon offsetting strategies

The urgent need to address climate issues increases the significance of sustainable strategies in power networks. This study focuses on the optimal operation of a hydrogen-based microgrid incorporated with photovoltaics (PVs), time-of-use demand response (TOU-DR) program, distributed generation (DG) units, hydrogen fuel stations (HFSs), and battery storage units. Employing a multi-objective approach, the study aims to minimize emissions and system operation costs, being validated by implementing it on the IEEE 33-bus test network. To address the carbon emissions issue, the Power-to-gas (P-to-G) facility is utilized to capture and recycle CO2 pollutions. The implementation of a carbon trading market mechanism is also adopted to further enhance the generation capacity of the PV units and facilitate the achievement of sustainable development goals. Besides, the presence of uncertainties in renewable generation, demand, operational characteristics of hydrogen vehicles, and market prices pose challenges for the system operator, which is addressed by employing the chance constraint programming (CCP) method. Results show that using the P-to-G system has led to a 10 % emissions reduction. However, this is accompanied by a corresponding 25 % increase in operational costs. Additionally, when operating under the CCP constraints, the system's storage level has been increased by 10 % to ensure optimal performance.

A robust state estimation by optimal placement of measurement units considering loads/renewable generations and measurements uncertainty

AbstractThe supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources

The integration of flexible loads, distributed energy resources, and other technologies is becoming common in advance power and energy systems. However, the integration also presents significant challenges due to the increasing complexity and uncertainty. To effectively manage these resources, an adaptive pricing mechanism is needed that can account for their variable availability. Based on this, we propose a new bilevel real-time pricing model that considers different distributed energy resources, uncertainty of renewable energy generation, carbon trading mechanisms, and grid fluctuations. Specifically, the upper-level optimization problem aims to maximize the total profit of the supplier that applies Q-learning algorithm. The lower-level optimization problem addresses the need for each user to make optimal power consumption decisions by constructing an individual Markov Decision Process framework for each user. The bilevel model achieves an effective balance of interests between the supplier and users by simultaneously considering both the upper-level and lower-level optimization problems. Additionally, our model can be efficiently solved using the distributed algorithm without the need to acquire transition probabilities. Simulation results show that the method is highly effective in balancing power supply and demand between the supplier and users, reducing carbon emissions, and mitigating power fluctuations.

Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %.

Valuation of distributed predictive information in robust economic dispatch

Robust economic dispatch is an essential way to deal with the uncertainties of renewable generations, but its performance highly depends on the pre-built uncertainty set. This paper proposes a novel robust economic dispatch model in which the operator can buy predictions from distributed forecasters to build a better uncertainty set and enhance its dispatch efficiency. The value of distributed predictive information can be quantified by the operator’s payment for buying it. The proposed model renders a two-stage robust optimisation problem with decision-dependent uncertainty (DDU). By analysing the structure of the uncertainty set, the proposed model is equivalently transformed into a two-stage robust optimisation problem with decision-independent uncertainty (DIU) so that it can be effectively solved by applying the column-and-constraint generation (C&CG) algorithm. Case studies show that the proposed method is effective.

Retracted: Resiliency Enhancement of Distribution Power Grids Using Mobile Marine Power Source

In this paper, a new configuration security framework is developed for reliability and resiliency improvement of distribution networks by the coordination of the security-constraint unit commitment (SCUC) and mobile marine power sources (MMPSs) under both post-disaster and normal restoration operations. It is assumed that a MMPS contains non-dispatchable distributed generators (DGs), e.g., photovoltaics (PV), as well as dispatchable DGs, e.g., gas turbines and diesel generators. A mixed-integer linear programming model is formulated for coordinating MMPSs and SCUC under both normal and extreme conditions (e.g., natural disasters, cyber and physical attacks). To better characterize the uncertainties in renewable generation and electric load, a deep learning gated recurrent unit is adopted to forecast the PV power output. The proposed model is tested on the IEEE 69-bus distribution network to validate the effectiveness and merits of MMPSs. Results show that reliability and resiliency could be enhanced by using MMPSs within the network.

Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot gurantee sufficient reserve deliverability in real-time operation. This paper proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to obtain the optimal solution with the lowest total cost including normal operation cost and battery degradation cost.

Rechargeable Lithium-ion and Aqueous Zn-based Battery

In the context of frequent extreme events around the world, economic and social development needs to utilize renewable energy. However, most renewable energy sources have the disadvantage of intermittency. It requires energy storage technology to balance supply and demand. Secondary battery energy storage technology can improve energy utilization efficiency and reduce the uncertainty of renewable energy generation. This paper introduces two kinds of energy storage technology, lithium-ion rechargeable battery and aqueous Zn-based rechargeable battery. Li-ion battery is prone to safety accidents such as combustion and explosion, and it will produce toxic substances. The aqueous Zn-based battery has high safety, and the production and using process is environmentally friendly. Compared with Li-ion, aqueous Zn-based has a lower area capacity, which is not conducive to large-scale commercial production applications. Due to the restriction of lithium resources, the production cost of lithium batteries is gradually increasing. In the future, in the development of energy storage technology, both technologies have certain durability and unique advantages, but there are also obvious limiting factors that need to be studied and improved.

Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving the stochastic optimal operation problem (OOP) in the IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, and pricing. The proposed I-WaOA utilizes three strategies: the fitness-distance balance method, quasi-opposite-based learning, and Cauchy mutation. The I-WaOA optimally locates and sizes photovoltaic (PV) ratings and wind turbine (WT) capacities and determines the optimal power factor of WT with DSR. Using Monte Carlo simulations (MCS) and probability density functions (PDF), the uncertainties in renewable energy generation, load demand, and energy costs are represented. The results show that the proposed I-WaOA approach can significantly reduce costs, improve voltage stability, and mitigate voltage deviations. The total annual costs are reduced by 91%, from 3.8377 × 107 USD to 3.4737 × 106 USD. Voltage deviations are decreased by 63%, from 98.6633 per unit (p.u.) to 36.0990 p.u., and the system stability index is increased by 11%, from 2.444 × 103 p.u. to 2.7245 × 103 p.u., when contrasted with traditional methods.

Operation optimization of electricity-steam coupled industrial energy system considering steam accumulator

Steam system plays a crucial role in industrial energy usage. Steam generation in the industry domain is transferring from coal-fired or gas-fired plant/boiler to green-electricity steamer for net-zero purpose. The increasing coupling of the electricity-steam energy system in the industry domain, called electricity-steam coupled industrial energy system (ES-IES), brings enormous challenges to the system day-ahead operation optimization due to the uncertainties of renewable generation and the nonlinearities of the system mathematical model. Therefore, this paper introduces the steam accumulator (SA) into the steam system to mitigate the renewable fluctuations, proposes an isothermal linearization method of the steam flow model, and optimizes the operation based on an interactive iteration scheme between optimization and simulation for the global optimum and a balance between accuracy and speed. Results show that considering the storage characteristics of SA and the complementary coordination of electricity and steam through coupling equipment can significantly optimize the operation of ES-IES with an increase in the renewable energy consumption rate by 23.81 % and a decrease in the total operating cost by 11.39 %. The static payback period for the investment of EB and SA is 7 years, indicating a favorable application prospect.

Unlocking inter-regional flexibility for the HVDC-connected two-area system with a multistage model

In recent years, the deployment of high-voltage direct current (HVDC) tie-lines in power grids has become a prevalent solution in some countries to transmit renewable energy from remote locations to load centers. However, the variability and uncertainty associated with renewable energy generation pose challenges to effectively utilizing this technology. This work proposes a novel multistage planning-operation model, aiming to unlock the potential flexibility in the HVDC transmission system and increase the renewable penetration. By incorporating flexibility, which is essential for accommodating the uncertainty in renewable generation, our model optimally shares the inter-regional flexibility between the sending- and receiving-end grids. One of the key features of our proposed model is its robustness and non-anticipativity, meaning it can account for different levels of uncertainty and make decisions that are suitable for multiple scenarios. This work develops two solution approaches to solve this challenging multistage model with variable uncertainty sets. We validate the proposed approach through a case study conducted on a real-world inter-regional grid. The numerical results demonstrate that our approach effectively unlocks more inter-regional flexibility and assists in increasing the renewable hosting capacity.

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

Probabilistic spatiotemporal scenario generation method for dynamic optimal power flow in distribution networks

Due to the intricate intertwining of spatiotemporal characteristics and the substantial computational complexity caused by high dimensionality, the simultaneous consideration of both spatial and temporal correlations is a non-trivial problem in power system scheduling with variable renewable energy sources. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method for the dynamic optimal power flow problem in distribution networks, which generates probabilistic scenarios considering the spatial and temporal correlations among random variables simultaneously. First, static scenarios are generated by Latin hypercube sampling, and the probability of each static scenario is determined by copula-importance sampling theory, which is proposed to ensure the static scenarios yield the same probabilistic characteristics as the random variables. Next, a probability-based scenario reduction technique is implemented to retain effective scenarios and lower the computational burden. Then, multiple linear regression generalizes the static scenarios into dynamic ones with the same probabilities. Finally, a spatiotemporal scenario-based stochastic optimization is proposed for the dynamic optimal power flow problem, which considers uncertainties in variable renewable energy generation and aims to balance the output of controllable generators, renewable energy curtailment, and load shedding. Numerical simulations in a modified IEEE 33-node distribution network show that the proposed approach outperforms the existing methods that do not simultaneously consider spatial and temporal correlations in terms of computational efficiency and accuracy, proving that the proposed PSTSG method can efficiently capture the spatial and temporal correlation so as to make the optimal scheduling decision.