Energy and Reserve Scheduling Under Wind Power Uncertainty: An Adjustable Interval Approach

Aiming at the need of peak-load shifting and the uncertainty of wind and photovoltaic power, this paper proposes a double layer virtual power plant scheduling model that integrates wind, photovoltaic power, gas turbine, power-to-gas device, energy storage devices and cooling heating and power microgrid. A Markov Chain Monte Carlo method is used to deal with the uncertainty of wind and photovoltaic power. In the upper layer VPP scheduling, the source side preferentially uses wind and photovoltaic energy, and the load side guides the load distribution through demand response measures such as TOU price and flexible load compensation. In the lower level VPP scheduling, the NSGA-II algorithm is utilized to the multi-objective optimization model by taking the minimum VPP operation cost and the lowest carbon emission as the objective function. The simulation results show that the proposed VPP scheduling model can effectively deal with the uncertainty of renewable energy output, and take into account environmental protection and economy of the system, so as to realize the low-carbon and economic operation of the system.

A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response

This paper presents a risk constrained economic dispatch model incorporating both the wind power and units' forced outage. The operation cost objective includes the costs of generation, spinning reserve, load shedding and wind spillage and the risk index is chosen as the expected load not served. A test system is adopted for illustration. The simulation results show that to decrease the cost (risk) to a relatively low value will dramatically increase the value of the risk (cost). Unit outage will increase both the cost and the risk values. The incorporation of wind power can relieve the increase in both the cost and the risk caused by unit outage. On the other hand, the wind power uncertainty will increase both the cost and the risk values.

Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power

Wind power generations as a renewable source of energy have become under the spotlight of policy markets and energy providers recently. However, the technical constraints, production variability and uncertainty of wind power have limited its extensive integration into power system. Having these limitations in mind, the present paper sets for a new methodology for increasing wind power integration in deregulated electricity market. The proposed method is presented with the consideration of voltage stability assessment and wind power uncertainties. Our plan aims at maximizing social welfare and minimizing investment and annual operation cost. At the same time, we seek to overcome line constraint by taking into account wind uncertainties. In order to fulfill the aims, FACTS devices and price sensitive loads are utilized. On the other hand, a multi-objective optimization problem was used to evaluate the annual costs and outcomes of wind power investment, the optimal setting of FACTS devices, and optimal load shedding. Expected Security Cost Optimal Power Flow (ESCOPF) is used to minimize the expected total cost of system operation. To solve the proposed planning problem, Non Dominated Sorting Genetic Algorithm (NSGA-II) is exploited. Our proposed method has been applied to an IEEE30 bus test system by considering two wind scenarios. The simulation results demonstrate the efficiency of the proposed planning.

As China's wind power has been expanding, the volatility, discontinuity and uncertainty of wind power have brought great difficulties to consumption of the wind power. With the development of smart grid, it becomes possible to improve the capacity of wind power consumption by introducing real-time price and demand response theory into the market. Firstly, theories of smart meters, real-time price, and demand side response were introduced. Then taking transfer cost into consideration, a demand response model based on real-time price is proposed to optimize the load curve. At last, the wind power consumption models were built respectively according to the load curves before and after the demand response. Results show that these models can effectively reduce the abandon of wind power and increase the amount of wind power consumption.

Recent research shows that non-convex AC OPF problem can be recast as a convex Semidefinite (SD) problem or Second Order Cone Programming (SOCP) problem. This paper presents a stochastic SOCP OPF (SSOCP-OPF) model for power systems connected to the wind farms. This is performed by reformulating the original non-convex OPF problem using more practical parameters of the power system. Finally, we obtain a convex optimization problem through some well-known approximations and an exact relaxation incorporating the stochastic nature of wind power. One of the advantage of convex SOCP problems, which are a general form of linear problems, is that they can be efficiently solved through Interior Point Methods (IPMs). The proposed OPF model takes advantage of both DC-OPF models (solution efficiency) and full AC-OPF models (solution accuracy). As an application of SSOCP-OPF model, we study the impact of wind power uncertainty on the transmission loss in the power systems. To evaluate the proposed stochastic model modified IEEE 30-bus test system is used. The optimization problem is coded in GAMS platform and solved using its embedded interior point optimizer MOSEK.

Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions

Dispatch results depend on the forecast wind power in an electric power grid. High levels of uncertainty in wind power lead to large forecast errors (FEs). Wind power FE creates an imbalance between power load demand and supply, which poses risks to the power grid. To minimise the risk caused by uncertainty in wind power, FE was analysed. The actual and forecast power of wind generations were studied to determine which factors had strong relationships with FE. After being analysed by principle component analysis, these factors were used to build an assessment model to estimate FE. According to the output of the assessment model, risk factor (RF) was defined to evaluate the risk‐level associated with wind power. Moreover, based on a grid including wind generation and fossil fuel‐fired power plants, a dispatch model was built to account for the cost of fossils fuels and RF. This dispatch model was studied to find strategies for decreasing the impact of wind power on the grid. The results from their research will increase the safety and reliability of power grids with uncertain levels of wind power and promote the widespread use of wind power.

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Short-term wind power interval prediction method using VMD-RFG and Att-GRU

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

With the large-scale wind power integration, the uncertainty of wind power poses a great threat to the safe and stable operation of the system. This paper proposes dynamic economic dispatch problem formulation in thermal power system incorporating stochastic wind and small-hydro (run-in-river) power, called thermal-wind-small hydropower system (TWSHS). Weibull and Gumbel probability density functions are used to calculate available wind and small-hydro power respectively. An improved differential evolution algorithm based on gradient descent information (DE-GD) is proposed to solve the dynamic economic dispatch (DED) problem considering uncertainty of wind power and small-hydro power, as well as complicated constraints in TWSHS. Based on the traditional differential evolution algorithm, the gradient information of the objective function is introduced after the mutation process to enrich the diversity of the population, thus increasing the possibility of convergence to the global optimization. Generation scheduling is simulated on a TWSHS with the proposed approach. Simulation results verify feasibility and effectiveness of the proposed method while considering various complex constraints in the thermal-windsmall hydropower system.

In the current power system dispatching, the high penetration of renewable energy sources has caused a lot of uncertainty. To cope with the issue, this paper proposes a data-driven stochastic optimization model as a solution to the unit commitment (UC) problem. This model integrates wind power and energy storage and considers the correlation among the forecast errors of multiple wind farms. Based on the Dirichlet process Gaussian mixture model (DPGMM), this paper first uses historical data to construct a probability density function (PDF) for the uncertainty and spatial correlation of wind power forecast errors. Then the simultaneous backward reduction (SBR) algorithm is used to select typical scenarios from the large number of simulated scenarios generated by sampling the PDF obtained previously. As such, the uncertainty of wind power is represented by the typical scenarios. After that, a stochastic UC model is proposed, the objective function of which is established considering the cost of wind curtailment and load shedding. Finally, taking a ten-unit system with two wind farms and energy storage devices as an example, the effectiveness of the above models and methods is validated by the results of comparative exemplifications.