Evaluation of Reliability Parameters in Micro-grid

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

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.

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.

This study presents a technoeconomic analysis of a hybrid wind‐PV (photovoltaic) power plant (HPP) compared to onshore wind power plants (WPPs) and photovoltaic power plants (PVPPs) in the Nordic electricity market, focusing on locations in Finland and Sweden. Wind power capacity has recently increased significantly in the Nordics, increasing the profit cannibalization of wind power. Renewable energy subsidies have been phased out in Finland and Sweden, thus new wind and PV power value creation is formed from the power market. The PV power capacity has also encountered significant growth in the Nordics. However, the capacity is still relatively low, allowing more revenue for produced PV power compared to wind power. The lower PV power profit cannibalization has increased interest in HPPs instead of WPPs. This contribution studies the economic feasibility of wind and PV power in changing market conditions in the Nordic electricity market. The market operation is modeled with three different configurations including selling all the power into the day ahead spot market and baseload or pay‐as‐produced power purchase agreement (PPA). In addition, a battery energy storage system (BESS) investment is analyzed using the operating strategy of shifting production to more profitable spot price hours. This study shows that due to the profit cannibalization and high cost of capital, the power plants are currently not profitable in the Nordic electricity market except when the bidding area has high average spot prices. The worst profitability was with WPPs when exposed to the market shape risk and with PVPPs when pay‐as‐produced PPA was agreed upon due to the higher levelized cost of electricity. However, the PV power profit cannibalization is expected to increase in the future as more PVPPs operate in the Nordic power market. Thus, the PVPP shape risk may increase in the future as well.

Distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG+ADN+MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for TG+ADN+MG multi-level co-optimization dispatch considering primary and secondary frequency regulation. To explore the synergistic ability of the TG+ADN+MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainties of wind power and load are well modeled by data driven, which is proposed in our previous work [1]. An improved 6-bus system is used to test the proposed model, the numerical results illustrate the effectiveness and efficiency of the proposed method.

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.

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

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.

Rolling optimization of transmission network recovery and load restoration considering hybrid wind-storage system and cold load pickup

Energy management plays a critical role to accomplish a reliable and efficient operation of a Microgrid (MG). As the power from renewable energy sources and load demand is uncertain energy management problem (EMP) of a MG becomes complex. This paper studies the EMP for a grid connected MG consisting of dispatchable distributed generators (DDGs), battery, and renewable generation such as wind power, respectively. In order to handle the uncertainties ascending from wind power and load demand, information gap decision theory (IGDT) is adopted in this paper. An illustrative case study is presented to demonstrate the applicability of the proposed method. Results show that least robustness is achieved on modeling uncertainty in both wind power and load demand in comparison to the modeling of uncertainty in either parameter.

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.

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

Renewable Energy Sources (RESs) are playing an important role in reducing carbon emissions of conventional fossil fuel, greenhouse gases and other pollutants. Moreover, they have a potential benefit of reducing energy cost and providing power to meet the energy demand. Nevertheless, grid stability is criticized via intermittent and non-controllable behavior of RESs. In this regards, coupling power generation units such as solar and wind with Energy Storage Systems (ESSs) is the most likely solution to pave the way for the outstanding transition from conventional power system to smart grid infrastructure. Nonetheless, this infrastructure requires models and controls for the implementation of next-generation grid architecture. Therefore, a virtualization of the above infrastructure could be overriding and cost-effective for implementation phase. In that regard, the present research proposes a hybrid micro-grid model including wind and solar energy, loads, a double auction market and ESSs. The proposed model is tested and validated using virtualization of the above proposed grid structure. The implementation of the virtualized system enhances the role of ESSs leading the power system to be stable. ESSs allow accumulating the surplus energy for later use in those periods in which wind and solar contribute to overproduction. Furthermore, it enables delivering back the extra energy in peak periods. Simulation results show the effectiveness of the proposed model and highlight the role of ESSs in stabilization. As a result, the proposed model can be used to test other scenarios such as implementation of controls and smart appliances of the new and improved power system augmented with RESs.

Based on the study of uncertainty and fluctuation of wind power, a novel algorithm including bad operation scenarios set and robust optimization method was proposed in the paper trying to solve probabilistic unit commitment problem of power system which has wind power generation. According to the power system probabilistic dispatch theory, the conservative indicator to evaluate the value of deterioration for bad scenarios set is established, and the variance is added in the optimization target to suppress some individual bad scenarios which may lead to deterioration of the overall optimization results. Furthermore, energy storage system, which has an inherent ability to mitigate active power fluctuation, was taken into account to contribute to the presented unit commitment models. Finally, the numerical example which includes cost, storage capacity, robustness metric and conservatism degree analysis verifies the effectiveness and practicality of the proposed methods.