Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage

ABSTRACTThe microgrid of the renewable energy sources are used as photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), micro turbines (MT), diesel generators (DG), and battery energy storage systems (ESS), offers a promising solution. The issues posed by microgrid operators (MGOs) in managing energy from multiple sources, device as a storage, and response demand programs are addressed in this research study, which proposes a finest dispatch of energy approach for connected grid and microgrid freestanding. In order to accomplish successful energy management, the suggested strategy takes into account not only the minimization of operational expenses but also the reduction of power losses and greenhouse gas emissions. For microgrid energy management (MGEM), a new multi‐objective solution integrating a demand response program is incorporated into a mixed‐integer linear programming model. The optimization issue illustrates the techno‐commercial benefits and assesses the effect of demand response on optimal energy dispatch. Furthermore, a hybrid optimization technique combining the African Vultures Optimization technique (AVOA) and Artificial Rabbits Optimization (ARO) is projected to holder the problem of optimization, and an optimized fuzzy interface is built for scheduling the ESS. Finding the best trade‐offs between costs, emissions, and power losses is made possible by the algorithm, which offers insightful information for the microgrid energy management system. The outcome of the renewable energy sources of the all categories are examined.

This article presents the innovative integrated control strategies of the battery energy storage system (BESS) to support the system operation of an offshore island microgrid with high penetration of renewable energy. An intelligent energy management system (iEMS) was implemented to perform the supervisory control and data acquisition of diesel generators, distribution feeders, photovoltaic (PV) systems, and the BESS. An expert system was designed and embedded in the iEMS to derive the decision making for fast power discharging of BESS to improve the system transient stability for the severe contingency of large disturbances caused by the tripping of generators and PV farm. To mitigate the impact of PV generation fluctuation and to maintain the stable operation of diesel generators, the adaptive control of power charging and discharging of the BESS is applied to reduce the ramping rate of PV generation. To achieve the economic dispatch of the microgrid, the strategy of peak shaving and valley filling of the system net load curve by the BESS was derived considering the forecasting daily profiles of system loading and PV power generation. The computer simulation of actual diesel generator tripping and the field test of intentional PV system shutdown have been executed to demonstrate the effectiveness of the proposed control strategies of the BESS system to improve the system operation and transient stability of the study microgrid with high penetration of renewable energy.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

This paper analyses the influence of Battery Energy Storage System (BESS) on the electrical system in a Hybrid Micro Grid consisting of two 200 kW diesel generators (DGs), one 200 kW wind turbine generator (WTG) and 200 kW photovoltaic (PV) Plant. The BESS in such a system can have a significant role in covering the power fluctuations in the network. The BESS can cover the power unbalance between the supplied power and the load of the grid by storing the excess energy or supplying the residual demand. An advantage of implementing the BESS is faster provision of power compared to the DGs which can also help improve a system stability. In this paper, the influence of BESS is analyzed by conducting Generation Adequacy analysis and Transient Stability studies using DIgSILENT Power Factory software. Generation Adequacy analysis is carried out using Monte Carlo simulation technique. Typical factors such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) are calculated. Furthermore, the size of BESS required to cover for the intermittent nature of renewables is determined. Dynamic models of DGs, WTG, PV and BESS are developed for the purpose of transient stability studies. Contingencies such as three phase fault, tripping of DG and cloud covering PV field are simulated with and without BESS in order to clearly determine its influence on system stability.

Guest Editorial: Situational awareness of integrated energy systems

Accelerated development of eco-friendly technologies, such as renewable energy (RE), smart grids, and electric transportation will shape the future of electric power generation and supply. The power consumption characteristics of modern power systems are designed to be more flexible and easily controllable, which will also affect the sizing of power generation system. This paper presents a methodology for the joint capacity optimization of a typical residential standalone microgrid (MG) employing RE sources, i.e., solar photovoltaic (PV), wind turbines (WTs), diesel generators (DGs), and battery energy storage system (BESS). The MG supplies a residential community load comprising of typical residential load plus electric vehicles (EVs) charging load. The realistic mathematical models of PV, WT, diesel generation system, BESS, and EV load are formulated to improve the capacity optimization methodology, which involves various realistic constraints associated with the RE sources, diesel generation system, BESS, and EV load. The labyrinthine optimization problem is formulated and solved innovatively to 1) minimize the cost; 2) reduce greenhouse gases (GHG) emissions; and 3) curtail dump energy. All three objectives have special significance in designing a standalone MG, for example, cost is related to the economics, GHG emissions deal with global warming, and dump energy is related to the stability and economics of the system. The optimization problem is solved for different possible combinations of PV, WT, DG, and BESS to determine the best possible combination to serve the load effectively and economically. In addition, the impact of load shifting on the sizes of distributed generators and BESS in terms of per-unit cost and GHG emissions is analyzed using the concept of controllable loads. This study could be assumed as a powerful roadmap for decision makers, analysts, and policy makers.

In this paper, stability of current control of battery energy storage system (BESS) connected with a diesel generator for a stand-alone microgrid are analyzed in four cases. The stand-alone microgrid system consists of 50kW BESS, 50kW diesel generator and controllable loads, where BESS is composed of 115kWh battery bank and 50kW DC-AC inverter. The four cases are 1) BESS with a stiff grid 2) BESS with the diesel generator 3) BESS with passive damping + diesel generator 4) BESS with active damping + diesel generator, and their stabilities are analyzed in the frequency domain and discrete time domain. Demonstration site for the stand-alone microgrid is constructed and comparative analysis for the four cases are verified through simulation and experiments, where experimental results show a good agreement with the analysis.

This paper analyzes the effect of battery energy storage system (BESS) on isolated power systems due to the increasing penetration of photovoltaic (PV) power plants. The degradation of the inertia system is a characteristic that is encountered when the penetration level of electronics-based power plants, like PV power plants, in a certain electricity system is high. As a result, faster frequency drift will occur following a contingency event, such as the loss of a generating unit. By injecting active power after a contingency event, BESS can support and improve system dynamic performance. The provision of dynamic frequency control support by BESS has been studied in the case of the island of Timor in Indonesia. The effect of BESS is analyzed by performing a digital simulation using the DIgSILENT Power Factory software. Work on impacting factors, such as BESS rated power, droop, and time activated reaction were attempted to discover out their effect on system. It appears that the BESS can improve the dynamic response of isolated power system.

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.

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

This paper presents centralized control for a wind farm using model predictive control (MPC) method. In order to solve intra-day unit commitment (UC) problem, the UC problem is solved by using the MPC method with short-term wind power forecasting. We introduce a new dynamics model for the UC with some constraints to utilize the benefits of the MPC method. The objective function considering the operation and maintenance costs is formulated by adding a new variable in order to average the operating time of each wind turbine (WT) within the whole time. The proposed method could solve the UC problems on-line using the prediction time horizon that could be selected flexibly considering time horizon based on wind power forecasting errors. From the simulation study using 10 WTs, we observed that the proposed dynamics model of UC effectively provided the optimal solution to each scenario. Numerical study will validate that the proposed method can be applied to solving the UC problem of a large scale wind farm by aggregating WTs.

The distribution system operator (DSO) needs an optimal day-ahead scheduling (ODAS) for the economic and sustainable supply of electrical energy considering input parameters such as the price of the upstream grid. Next-generation distribution networks or smart distribution networks are the future networks where responsive loads are available. Renewable sources include wind turbines and solar panels have expanded. The presence of electric vehicles and the purchase from the electricity upstream market is provided, and the network can be programmed and controlled through the devices which record and transmit information. In this chapter, a robust optimization (RO) method has been proposed to minimize the cost of ODAS of smart distribution system (SDS) considering load-responsive and renewable energy sources (RESs) such as wind turbine (WT) and nonrenewable sources such as diesel generators (DGs) and battery energy storage system (BESS). The proposed method considers all the technical constraints of the upstream grid and DGs and the utilized BESS. In order to model SDSs, a 33-base IEEE test system has been used in the evaluation of the proposed model. The proposed ODAS concept determines the optimal level of exchange with the upstream network, the production of each dispersed generation unit, and the participation of demand response (DR) programs. It also provides an optimal layout for charging and discharging the BESS. It can be observed that the proposed model has the optimal scheduling capabilities of the SDSs considering the uncertainties of power market price. Moreover, it is observed that the resilient optimization method reduces the cost of network operation and confronts the price of electricity with uncertainty.

As an attempt to reduce reliance on fossil fuels to satisfy electricity demand, the penetration of renewable sources like wind or solar power has experienced rapid growth in recent years. Due to their intermittent nature, the exact contribution of renewable sources to electricity supply is at least partly unknown. In order to be able to accommodate this uncertainty in real-time, sufficient capacities in the form of thermal and hydro power plants must be available. On that account, power system operators solve the so-called unit commitment problem after bidding in the day-ahead market closes to derive schedules for their power plants which can ensure system reliability at low costs. In literature, two approaches have mainly been studied in this context: Robust and stochastic unit commitment. Being a worst-case formulation, robust unit commitment puts its focus on reliability. A common drawback of this approach is that it tends to deliver over-conservative schedules. Stochastic unit commitment on the other hand creates more cost-effective schedules by preparing for the expected case, but either fails to guarantee system reliability or puts a high workload on the CPU. The limitations of known formulations have sparked interest in so-called hybrid approaches, which aim at combining the ideas of robust and stochastic unit commitment in a favorable way. This paper reports a novel hybrid approach to solve the unit commitment under uncertainty, which yields both robust and cost-efficient schedules. The new method respects the continuous nature of uncertainties and is thus in particular favorable for applications in power systems with high penetration of volatile renewable sources. By merging the ideas of robust and stochastic unit commitment, the proposed hybrid formulation minimizes the expected worst-case dispatch costs. Our method relies on partitioning the continuous range of the uncertainties into subsets. By means of the number of partitions, the solution can be adjusted between the conservative robust and the cost-efficient stochastic unit commitment in a user-friendly manner. A Benders decomposition algorithm is derived to solve the hybrid unit commitment efficiently. Finally, a case study confirms the superior performance of the proposed method.

This paper discusses the unit template control strategy for load balancing and voltage regulation of induction generator based DG (Diesel generator) set for islanded system. The proposed system consists of DG set along with DSTATCOM (Distributed Static Compensator) and BESS (Battery Energy Storage System) is used for load balancing along with harmonics elimination and reactive power compensation. An isolated T-T Connected transformer is used to reduce size of two-leg VSC and also neutral current compensation. The battery on two-leg VSC (Voltage Source Converter) of DSTATCOM with BESS is able to supply active power when the load requirement is more than rating of DG set and battery can store energy when load requirement is less than DG set rating. The reference sources for two-leg VSC are estimated using unit template control strategy and this algorithm has quick dynamic response. The proposed DG set system with DSTATCOM is modeled in MATLAB R2009a along with Simulink and results are verified under three phase linear/non-linear loads.

Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid