Security-constrained unit commitment with large-scale cross-region wind penetration

Considering wind farm integrations, a robust optimization model for security-constrained unit commitment (SCUC) is built to deal with the errors on wind power predictions. In the proposed formulation, spinning reserve requirement is specially considered to support possible wind power change between two successive time iterations. Network security constraints are considered by DC power flow equations. Moreover, an iterative method is proposed to solve the SCUC problem considering network losses by using loss factors and loss distribution factors. Case studies on the IEEE 30-bus system demonstrate the effectiveness of the proposed method. The results under different wind integration scenarios are also compared.

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

The construction of offshore wind farms in Northern Europe commenced in the early nineties. In the beginning, only minor test offshore wind farms were built, but from 2002 and onwards, large-scale offshore wind farms have been erected. Construction and operation experiences from these wind farms are now being collected, and this paper discusses construction and operation experiences obtained from selected offshore wind farms. Differences and similarities between the wind industry and the oil and gas industry are discussed with a view to raising the awareness of this new and growing industry among market players in the oil and gas industry. Current status and plans in Europe [1] In the European Union (EU) the development of wind power is very impressive. Over the past five years, 30% of all newly installed electricity generating capacity has been wind power. The installed capacity is now 40.5 GW, and the generated power is enough to supply 21 million households. However, wind power still only covers 2.8% of the total European electricity demand. Only 680 MW of the above-mentioned 40.5 GW capacity are installed off shore. Recent projections of the future development suggest that by 2030 the installed capacity may reach 300 GW of which 150 GW are expected to be installed off shore. Whether the targets for offshore wind power will be met depends crucially on technology development, grid infrastructure and the existence of political incentives to encourage investments. A renewables target of at least 20% of EU energy by 2020 will be a significant driving force behind this development. It will only be possible to meet this target with a strong contribution from wind power. Another driving force behind the wind power development is the increase in fossil fuel prices. Current status and plans in Denmark [2] Denmark has been and is still a frontrunner in the development of on- and offshore wind energy. Only in Denmark, Spain, Germany and Ireland does wind power cover more than 5% of the electricity demand. In Denmark, the actual figure is close to 20%. The installed capacity is 3100 MW of which 420 MW are located off shore (two large-scale wind farms contribute 325 MW). Recent wind farm developments have been located off shore, and future plans aim to expand offshore wind power event further. Currently, investment decisions to erect two offshore wind farms of 200 MW each are in the pipeline. Challenges going off shore The main reasons for going off shore are access to large energy resources and potentially less complicated planning processes. Although the wind resources are vast, the investment in foundations and electrical infrastructure (cables and substations) are dramatically higher than for onshore wind farms, and operation costs increase as well. At first glance, the planning process, including the environmental statements, seems to be easier off shore, but experience shows that there are many stakeholders in the offshore areas, who make the offshore process as difficult as the onshore process.

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.

Aiming at the large-scale wind power transmission end grid wind and thermal power shared transmission channel decoupling out, the wind power output power is blocked, the transmission channel utilization rate is not high and so on. Based on a comparative analysis of wind and fire electrolytic coupling / coupling transmission operation characteristics, this paper The goal is to maximize the output power of wind power, maximize the utilization of transmission channels, and minimize the operating costs of thermal power units. Aiming at the dispatching output of wind power and thermal power units as optimization variables, a multi-objective optimization model of coupled wind power and thermal power transmission is proposed. The target differential evolution algorithm solves the model, and finally obtains a set of optimized scheduling output plans for wind and thermal power, which provides a scientific basis for operators to make scheduling decisions. Finally, a regional power grid is taken as an example to prove the rationality of the model and the effectiveness of the proposed algorithm.

A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements

In order to more efficiently and reliably carry out the joint operation of hydropower, wind power and photovoltaic power in large watershed scale, the joint operation of three kinds of energy is studied in this paper. In this paper, 3 reservoirs and 12 power stations below the two estuaries in the middle and lower reaches of Yalong River Basin are selected as the research object, and the model is constructed with the objective of optimizing the stability of water, wind and electricity, maximizing the total power generation and minimizing the total waste power of water, wind and electricity power supply, which is calculated and analyzed by step-by-step optimization algorithm, the optimal capacity matching relationship that managers can adopt in the case of an example is obtained. The results show that the optimal criterion for the stability of water, wind and electricity proposed for the large-scale water, wind and clean energy complementary base can effectively regulate and compensate the large-scale fluctuation of wind power and photoelectric output, provide safe and stable high-quality power supply for the power grid, help to better carry out the joint dispatching of hydropower, wind power and photovoltaic power in the basin and improve the consumption level of hydropower, wind power and photovoltaic power in the basin. This paper can provide a new idea for the study of cascade hydropower, wind power and photovoltaic power generation complementary joint operation in large-scale watershed.

In recent years, wind power and photovoltaic power generation have developed rapidly, and the installed proportion of wind power and photovoltaic power will further increase in the future. Aiming at the strong uncertainty of wind power and photovoltaic power, a scene generation method of wind and solar active power output based on k-medoids clustering and generative adversarial networks is proposed to avoid problems of model convergence and gradient disappearance, meanwhile preserving the diversity of generated results. Using the active power output data of two wind power plants and six photovoltaic power plants in Western China for 365 days a year, the data samples are clustered into ten scenarios by k-mediods method, which are used as training sets under different typical scenarios. The distribution characteristics of active power output data are effectively fitted by the generative adversarial networks, and its generation effect is analyzed. The results show that the scene generation method of wind and solar active power output based on k-medoids clustering and generative adversarial networks has the advantages of fast convergence and high precision.

Wind power is one of the most rapidly growing clean and renewable energy sources. However, due to the uncertainty and intermittency of wind power, the increasing penetration of wind power into the electric power system will pose challenges to power system operators. Moreover, as a participant in a competitive electricity market, a wind power producer's behavior and profit will be influenced by other participants' behaviors. This paper proposes a model of using stochastic programming to generate optimal bidding strategies to maximize the total profits of wind and conventional power producers in both the energy market and a bilateral reserve market, where the reserve price is settled between wind and conventional power producers by using game theory. Case studies using real-world data for games in an electricity market with different types of players are performed to show the effectiveness of the proposed model.

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

In this paper, a new formula for AC unit commitment problem with safety constraints is proposed, which can express the uncertainty of wind power with bounded interval. The proposed SCUC model can flexibly adjust the width of the interval by selecting an appropriate confidence level, so as to achieve a good compromise between security and economy. In addition, the proposed SCUC model is equivalent to a two-stage stochastic programming problem in one expected case, while the second stage represents the corrective operation in two extreme cases. In order to deal with the large-scale and integer mixed nonlinear characteristics of the model, a new calculation strategy based on generalized Benders Decomposition is developed to solve this problem and obtain robust commitment plan to withstand the worst-case realization of uncertain wind energy. The proposed SCUC model is illustrated by IEEE 9 bus and 118 bus test system, and its advantages over the existing random programming technology are verified. The simulation results show that the proposed method is applicable to UC problem with uncertainty of wind power generation.

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.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a security-constrained unit commitment (SCUC) algorithm which takes into account the intermittency and volatility of wind power generation. The UC problem is solved in the master problem with the forecasted intermittent wind power generation. Next, possible scenarios are simulated for representing the wind power volatility. The initial dispatch is checked in the subproblem and generation redispatch is considered for satisfying the hourly volatility of wind power in simulated scenarios. If the redispatch fails to mitigate violations, Benders cuts are created and added to the master problem to revise the commitment solution. The iterative process between the commitment problem and the feasibility check subproblem will continue until simulated wind power scenarios can be accommodated by redispatch. Numerical simulations indicate the effectiveness of the proposed SCUC algorithm for managing the security of power system operation by taking into account the intermittency and volatility of wind power generation. </para>

Difference between grid connections of large-scale wind power and conventional synchronous generation