Scenario Generation Method Research Articles

A Day-Ahead Chance Constrained Volt/Var Control Scheme With Renewable Energy Sources by Novel Scenario Generation Method in Active Distribution Networks

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.

A hybrid electricity price scenario generation method for stochastic virtual bidding in the electricity market

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios. This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average (SARIMA) model and historical data. The electricity price spikes are first identified by using an outlier detection method. Then, the historical data are decomposed into base and spike components. Next, the base and spike component scenarios are generated by using the SARIMA- and historical data-based methods, respectively. Finally, the electricity price scenarios are obtained by combining the base and spike component scenarios. Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method. The optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method, the SARIMA method, and a historical data-based method, respectively. Case study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and, therefore, can help the virtual bidder earn more profit in the electricity market.

Data Driven Stochastic Energy Management for Isolated Microgrids Based on Generative Adversarial Networks Considering Reactive Power Capabilities of Distributed Energy Resources and Reactive Power Costs

This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

Analysis of Solar and Wind Power on Access Planning of Multiple Renewable Energy Sources

The scenario generation method suitable for solar and wind power obeying any probability distribution and the concept of cluster was brought up to improve the consumption ability of distribution network of medium and high voltage for renewable energy. The planning model of renewable energy access was established based on cluster partition considering the investments and power generation interests of power producers as well as the power match degree within clusters. The Benders decomposition method was employed to solve the planning problem, and the complicated planning problem of renewable energy generation is decomposed into the decomposed investment operation model. Therefore, the access planning of renewable energy taking account of the solar and wind power was realized.

Forecasting Scenario Generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

Optimal demand response resource exploitation for efficient accommodation of renewable energy sources in multi-energy systems considering correlated uncertainties

Abstract This paper proposes a comprehensive planning model for enhancing accommodation of renewable energy sources into a combined heat and power based multi-energy system by utilizing the flexibility of a demand response program. As distinct from existing works, demand response has been implemented through a price-based program not only in terms of its effects on system operation but also for the planning decisions, and the potential correlations among uncertainties (i.e., renewable energy sources availability, load demand, and demand-side responsiveness) have been explicitly considered in our study. The concerned problem is interpreted into a two-stage optimization problem, where the placement of advanced metering infrastructures, the installation of renewable generation units along with the relevant pricing strategy for the demand side are jointly optimized to minimize the overall economic costs of the system. The flexibility of customers’ energy demands in response to real-time price changes is represented by using a generalized elasticity model, through which the demand response in terms of both load shifting in the temporal dimension, and the switching of energy source on the demand side can be properly captured under a unified framework. An efficient scenario generation method leveraging a series of correlation-handling techniques is employed to address the uncertainties in the proposed problem and a comprehensive scenario reduction method based on a novel optimal clustering technique is further introduced to alleviate the computational burden of the resultant model with the correlated uncertainties. Compared with the existing literatures, the novelty of this paper is in three-fold: 1) This study investigates the potential value of demand response for promoting renewable energy exploitation from a long-term planning perspective, instead of the conventional operation aspect. 2) The impacts from both system uncertainties and their potential correlations have been explicitly considered. 3) A comprehensive scenario reduction method based on optimal clustering analysis is introduced and employed to alleviate the solving complexity and improve the computational efficiency of the proposed model. The proposed planning framework is demonstrated on an illustrative test case and the simulation results verified the effectiveness of the developed model.

Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China

The reused batteries have become a practical alternative to household energy storage system, which is conducive to the effective utilization of excessive roof photovoltaic power generation and the sustainable development of energy. Economic incentives are the driving force for residential consumers to develop photovoltaic and energy storage. This study combines a solar-load uncertainty model and economic analysis to assess the financial impact of adding a reused-battery energy storage system to a photovoltaic assemblage in the context of multi-tariff policies and photovoltaic resource regions in China. First, we classify the types of residents based on the correlation between the users’ electricity consumption behavior and solar radiation. Secondly, to characterize the solar-load uncertainty, a deep scenario generation method based on an improved variational autoencoder is proposed to generate solar-load scenarios. Then, a mixed-integer linear programming model is developed which takes solar-load uncertainty into account. Finally, the operating cost of photovoltaic with a reused-battery energy storage system for each type of residential user under multi-tariff policies in China considering solar load uncertainty is obtained. The results demonstrate that the generated scenarios can effectively describe the uncertainty of the photovoltaic output and residential load. And, the correlation between the users’ electricity consumption behavior and solar radiation can guide the residential customer to install the reused-battery energy storage system. Moreover, economic feasibility and sustainable development of photovoltaic with reused-battery energy storage system depending on the regulation of market tariff policy.

Risk-controlled economic performance of compressed air energy storage and wind generation in day-ahead, intraday and balancing markets

In this paper, the wind power aggregator is cooperating with a commercial compressed air energy storage (CCAES) to participate in three markets, including day-ahead (DA), intraday (IN), and balancing (BL) markets. A three-stage stochastic programming problem is formulated to model the optimal operation of the proposed system. In the proposed model, the uncertainties of wind power, as well as DA, IN, and BL markets price is modeled by the implementation of scenario generation and reduction methods. Financial risks imposed from the uncertain parameters are investigated in the proposed stochastic optimization framework. For this purpose, the downside risk constraints approach (DRCA) is modeled in the stochastic formulation to manage the financial risks. In the proposed DRCA, a risk-based strategy can be proposed for the aggregation of CCAES and wind as a power aggregator (PA) that has a controlled risk in the risk-averse strategy. According to obtained results, the expected profit of PA without DRCA is € 4176.3, while the proposed strategy by the DRCA leads to a € 4094.0 profit. Therefore the proposed strategy by DRCA has a lower profit (1.97%) while leads to a guaranteed risk-controlled strategy for PA with the reduced risk by 100%.

A new wind speed scenario generation method based on spatiotemporal dependency structure

The accuracy of wind speed scenario generation is critical to the energy dispatching and planning of the wind-integrated power system. The accuracy of the modeling of wind speed can be improved with a comprehensive consideration of the dependency structure between wind speeds, and then the accuracy of wind speed scenario generation can be improved. Therefore, spatial and temporal dependence have been widely studied. However, linear correlation does not fully reflect the nonlinear nature of wind speed. Tail dependence, as a kind of non-linear dependency structure of wind speed in space and time, is studied in this paper. This paper proposes a wind speed scenario generation method based on the C-vine copula that considers spatiotemporal tail dependency structure. With the consideration of spatiotemporal dependency structure, the number of modeled random variables increase significantly. Therefore, a two-step wind speed scenario generation method is proposed to avoid the dimensional disaster. The wind speed data of two wind farms provided by the National Renewable Energy Laboratory are used in simulation analysis, the results demonstrate that reasonable consideration of the spatial and temporal tail dependence of wind speed can improve the accuracy of the spatial and temporal models, and further improve the accuracy of scenarios.

Review of wind power scenario generation methods for optimal operation of renewable energy systems

Scenario generation is an effective method for addressing uncertainties in stochastic programming for energy systems with integrated wind power. To comprehensively understand scenario generation and optimize solutions for uncertainties, the various methods and applications of scenario generation are classified and discussed in this work. First, the basic concepts are presented and scenario generation methods for addressing stochastic programming problems are discussed. Second, three categories of scenario generation methods are briefly introduced, along with their derived methods, advantages, and disadvantages. Third, an evaluation framework for these methods is established. Subsequently, applications of the scenario generation methods in power systems are discussed to identify the properties of these methods. Further, a comparative analysis and discussion are presented to show the suitability of each scenario generation method and to help choose the appropriate methods for different practical situations. Finally, the current limitations and future works with regard to scenario generation for stochastic programming in wind-power-integrated systems are highlighted and discussed. The results of this study are expected to provide references for applying scenario generation methods to the optimal operation of renewable energy systems.

Real-time optimal power flow solution for wind farm integrated power system using evolutionary programming algorithm

In order to satisfy the increasing demand for electricity, a huge burden is levied on the conventional coal-fired thermal power generation units, which affects the environment and human health adversely. To tackle this problem, the clean renewable sources of energy have gained more attention. With time, wind power is expected to contribute more significantly and should be used as much as possible. This has provided much focus to the wind farm integrated power system. But, the unsteady behavior of wind energy has made the operation of wind farm integrated power system more complex. Scenario generation methods are used for tackling the unsteady behavior of wind energy. A persistence method of scenario generation is proposed for solving real-time optimal power flow problem in wind farm integrated power system. The proposed persistence method performs better compared to other recent methods in the literature.

LSTM auto‐encoder based representative scenario generation method for hybrid hydro‐PV power system

The increasing penetration of renewable energy sources causes complex uncertainties of the power system. To capture such uncertainties in power system planning, an important step is to generate representative scenarios. In this work, a long short term memory (LSTM) auto-encoder based approach is proposed to generate representative scenarios in an integrated hydro-photovoltaic (PV) power generation system, which consists of feature extraction by LSTM Encoder, scenario clustering in feature domain by combining gap statistics method and K-means++, and representative scenario reconstruction by using LSTM Decoder. Compared with traditional scenario selection and generation methods, the proposed method can better capture the patterns of multivariate time-series data in both temporal and spatial dimensions. A case study in southwest China is used to demonstrate the effectiveness of the proposed method, which outperforms other existing methods by achieving the lowest SSE and DBI indices of 0.89 and 0.12, respectively, and obtaining the best SIL and CHI scores of 0.93 and 2.30, respectively, In addition, the case study shows the proposed model setup works more stable for scenario generation.

Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy

Efficiently using multiple energy sources, including renewable energy, is a focus of applied energy research. A regional integrated energy system (RIES) involves coupling use of multiple energy sources, which can improve energy efficiency using multi-energy complementarity. However, increasingly renewable energies and multi-energy sources load access into an energy system increase the multiple uncertainties of RIES, which has considerable impact on both system planning and operation. This study proposes a multi-objective stochastic planning method that is based on the multi-dimensional correlation scenario set generation method for RIES’ expansion planning. The scenario generation method considers the characteristics, time sequence, autocorrelation, and cross-correlation of renewable energies and multi-energy loads. The pipeline risk index for energy network expansion planning is defined considering the energy pipeline’s importance. A multi-objective stochastic planning model based on chance constraints of the energy network is developed to minimize the investment cost and the energy pipeline risk. Finally, to confirm the effectiveness and the applicability of the proposed model and method, certain numerical cases at Yangzhong City, China, are simulated. Two RIES expansion planning scenarios are then compared. Moreover, the Pareto fronts of the optimized expansion planning schemes are demonstrated, providing reference for balancing the energy network planning scheme economy and the energy pipeline risk.

Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power

Using the improved flexibility from the district heating system with multiple flexible devices is an effective solution to ensure the cost-effective and secure operation of the integrated energy system with high penetration of renewables. This paper exploits the improved flexibility of the integrated electricity and heat systems by using reserves from multiple flexible devices to accommodate more wind power and reduce operational costs. A two-stage stochastic optimal dispatching scheme is proposed for the integrated electricity and heat system considering both power networks and heat pipelines, and reserves from the condensing combined heat and power units, heat pumps, electric boilers, and heat storage tanks. The proposed scheme balances the power and heat sectors both in the day-ahead and real-time stages with the synergy of different flexible devices and linkage for each device in the two stages. A scenario generation method considering spatial-temporal correlation is proposed to provide reasonable wind power profiles for the two-stage dispatch scheme. The Gaussian mixture model and exponential function are used to construct the spatial and temporal correlation, respectively, and the Gibbs sampling is utilized to reduce the sampling complexity. The case studies were conducted on a 6-bus integrated electricity and heat system and a practical integrated energy system in Northern China. The results show that utilizing the scenario set with spatial-temporal correlation and improved flexibility can effectively reduce the operational cost and wind power curtailment.

Scenario tree construction driven by heuristic solutions of the optimization problem

We present a new scenario generation process approach driven purely by the out-of-sample performance of a pool of solutions, obtained by some heuristic procedure. We formulate a loss function that measures the discrepancy between out-of-sample and in-sample (in-tree) performance of the solutions. By minimizing such a (usually non-linear, non-convex) loss function for a given number of scenarios, we receive an approximation of the underlying probability distribution with respect to the optimization problem. This approach is especially convenient in cases where the optimization problem is solvable only for a very limited number of scenarios, but an out-of-sample evaluation of the solution is reasonably fast. Another possible usage is the case of binary distributions, where classical scenario generation methods based on fitting the scenario tree and the underlying distribution do not work.

Co-Optimized Parking Lot Placement and Incentive Design for Promoting PEV Integration Considering Decision-Dependent Uncertainties

This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentive policy on PEV owners’ charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on an illustrative test system verify the effectiveness of the proposed model and the approach.

The value of coordinated hydropower bidding in the Nordic day-ahead and balancing market

Modern power markets often consist of a series of sequential markets where power is traded. Market agents must coordinate their trading strategy across all markets. In this paper, we formulate the mult-market bidding problem for power producers as a stochastic program. This formulation is implemented within the framework of optimization models used by the Nordic hydropower industry. We also present how input to the stochastic program may be generated by using a forecast-based scenario generation method combined with time-series models that predicts future prices and turnovers in the markets. The model is applied in a case study to investigate the value of participating in the Nordic day-ahead and balancing market. A producer may participate in the balancing market either by considering the markets sequentially or coordinated. Using cases with limited and perfect information about the balancing market to calculate lower and upper bounds, we find that the value of participating in the balancing market using the sequential approach is between 0.8 and 2.6%. Using the coordinated approach, the producer may gain between 1.4 and 2.9%. The value of coordination, i.e. the value of using the coordinated over the sequential approach, is found to be higher in the limited information case (1.7%) than in the perfect information case (1.1%). This indicates that, the more uncertainty, the higher is the value of coordination.

A clustering-based scenario generation framework for power market simulation with wind integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

A new method to generate daily load scenario of electric vehicle charging station considering time correlation

Abstract Due to the influence of random factors such as travel behavior of car owners and traffic condition, the electric vehicle (EV) charging station load has strong randomness. Establishing an appropriate probability model to describe the stochastic of EV charging station load is of great significance to the safe operation analysis of distribution network. Therefore, this paper proposes the probability modeling and the scenario generation method for EV charging station load based on historical data. Firstly, the load of each period is regarded as a random variable, and the probability distribution model of each random variable is obtained by fitting historical data. Secondly, according to the analysis of load correlation, 96 periods of a day are divided into several sets of adjacent periods. Considering the correlation between different period loads in each set of adjacent periods, the Pair-copula method and the D-Vine structure is used to obtain the joint distribution model in each set of adjacent period loads is obtained. Thirdly, according to the joint distributionmodel, all sets of adjacent periods of a day and the corresponding load scenarios are obtained, and the daily load curve considering time correlation are generated. Finally, referring to the actual historical load curve data, the effectiveness of the proposed method in this paper is verified by comparing with the daily load curve, which is generated based on the independent distribution model of each period load without considering time correlation.

Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method

The wind–heat conflict and wind power uncertainty are the main factors leading to the phenomenon of wind curtailment during the heating period in the northern region of China. In this paper, a multi-time scale optimal scheduling strategy for combined heat and power system is proposed. Considering the temporal dependence of wind power fluctuation, the intra-day wind power scenario generation method is put forward, and both day-ahead and intra-day optimization scheduling models based on the scenario method are established to maximize the system’s revenue. The case analyzes the impacts of the initial heat storage capacity of a heat storage device and different scheduling strategies on system revenue. It is verified that the scheduling strategy can better adapt to wind power uncertainty and improve the absorption capacity of wind power, while ensuring the safety and economical efficiency of system operation.