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

Two demand response (DR) programs, namely, load reduction demand response (LRDR) program and load transfer demand response (LTDR) program are both incorporated into the day-ahead scheduling in this paper. The LRDR participants provide load curtailment in energy scheduling and reserve capacity in spinning reserve scheduling while the LTDR customers participate in energy scheduling by providing load shift resources. Compared to basic DR models, the proposed models of LRDR and LTDR are extended with a refined description by adding various constraints in the offer package. The actual operation characteristics of DR resources are reflected in the models. Besides, the day-ahead scheduling mode with DR is studied. Finally, a co-optimized day-ahead energy/reserve scheduling model integrated with DR which simultaneously schedules energy and reserve is established. The effectiveness of the proposed day-ahead scheduling model is verified on a modified IEEE 118-bus system.

RETRACTED: Robust optimization of renewable-based multi-energy micro-grid integrated with flexible energy conversion and storage devices

This paper presents an adjustable interval optimization model for the energy and reserve clearance while treating the wind power variation and uncertainty. Instead of conventional predicted intervals (PIs), adjustable intervals (AIs) as subsets of PIs are proposed as more judicious choices for economically covering wind uncertainties. Ramp-capability reserve is modeled to ensure the existence of sufficient ramp capability needed to follow up wind power variation within AIs. However, realization of wind power beyond AIs, which is no longer compensable by ramp-capability reserve, would necessarily lead to either load shedding or wind spillage. In order to optimally determine AIs, apart from the energy and reserve procurement costs, the costs associated with load shedding and wind spillage are incorporated in the objective function. The confidence level theory is employed as well for tuning the robustness and conservatism of the final solution. Performance of the proposed method is examined on several case studies on an 8-bus and modified IEEE 118-bus test systems. Sensitivity analyses are conducted to specify the impacts of important parameters on the obtained solution. The results confirm the applicability and effectiveness of the proposed methodology.

For optimal microgrid (MG) operation, one challenge is the supply of cooling and electricity energy is a coupled co-optimization issue when considering the combined cooling, heating and power (CCHP) units and ice-storage air-conditioners. Another challenge is the inherent randomness of renewable energy within the MG should be accommodated by MG itself. In Part I of this two-part paper, the partial load performance of CCHPs and the performance of ice-storage air-conditioners are modeled, and the cooling and electricity coordinated MG day-ahead scheduling and real-time dispatching models are established. In day-ahead scheduling model, the uncertainty of wind and solar power is represented by multi-scenarios and the objective is to achieve the minimal expected MG operation cost. In real-time dispatching model, the different time-scale dispatch schemes are respectively applied for cooling and electricity to smooth out the fluctuations of renewable energy supply and to follow the variations of cooling and electricity demands by the fine dispatching of the components within MG such that the impact of MG to the connected main grid is minimal. The proposed MG multi time-scale cooling and electricity coordinated schedule achieves an integrated optimization for multi energy-type supply, and makes the MG be controllable as seen from the main grid.

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.

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 recent years, frequent occurrence of extreme weather events has caused great losses to the power system. Meanwhile, the high penetration of renewable energy has brought new challenges to the safe operation of the system. Coordination of various measures against different stages of disaster can effectively reduce the overall loss and enhance system's resilience. This paper proposed a methodology to coordinate preventive and emergency dispatch stages based on the defender–attacker–defender model, in which various dispatching measures are modelled as defenders, whereas the extreme weather event and wind power's uncertainty are modelled as attackers. Specifically, a two‐stage three‐layer robust optimal dispatching model is established, in which conventional generator's output, wind farm's grid‐connected planning, and load shedding are taken as the main dispatching measures. To solve this model, the column and constraint generation algorithm is used to transform the three‐layer optimization model into a mixed integer linear problem . The effectiveness of the proposed model is verified in the modified IEEE RTS‐79 test system, and the results show that the coordination of preventive and emergency dispatch can effectively reduce the load outage cost under extreme weather, thus enhancing the ability of power system to cope with extreme scenarios.

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.

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

This paper proposed a novel dispatching model to schedule the spinning reserve plan which is minimum required for coping with wind power uncertainty. The proposed model is robust to accommodate multiply scenarios of wind power, including normal and extreme scenarios. Different from other robust model, this model does not contain any intractable function in objective function or constrains, such as max-min function or chance constrain. The proposed model describes the uncertain characteristics of wind power with a series of conventional deterministic constraints which are convenient to solve. The mathematic formulate of the model is detailed and its advantage is analyzed by compared with other model in mathematic. Simulative results show that the scheduling plan can satisfy the possible scenarios of wind power with minimum positive spinning reserve and negative spinning reserve, which coordinate the robustness and economy of system's running.

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.

Renewable energy resources, notably wind power, are expected to provide considerable portion of the world energy requirements in the near future. Many system operators around the world are challenged by the problems associated with integrating these intermittent resources into the grid. As one of the potential solutions, demand response (DR) is expected to play a major role for mitigating integration issues of intermittent renewable energy resources. In this context, this paper proposes a DR program which helps to integrate wind power by reshaping the load of the system. The DR program provides a framework to procure load reduction from DR resources in the wholesale energy market. The participants in the program submit their offer packages to provide load reduction in the day-ahead energy market. A day-ahead network-constrained market clearing formulation is also proposed which considers the load reduction provided by the DR program participants as an energy market commodity. The proposed method, which is in the mixed-integer linear programming format, determines commitment state of generating units, schedules the energy and spinning reserve provided by generating units, and schedules the load reduction provided by the DR program participants. To reveal the features of the proposed method, several numerical studies are conducted on the IEEE-RTS. The results presented indicate that integrating load reduction in the energy market provides a powerful tool to selectively modify the system load to support wind power integration, while making significant economic and technical benefits for the system.

Multi-timescale coordinated schedule of interdependent electricity-natural gas systems considering electricity grid steady-state and gas network dynamics