Output Power Of Renewable Energy Sources Research Articles

Two-Area Automatic Generation Control for Power Systems with Highly Penetrating Renewable Energy Sources

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system’s frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events.

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.

Novel Curtailment Control Strategy for Wind Power Plants Based on the SQP Optimization

As the penetration level of renewable energy sources (RESs) increases, the output power of RESs needs to be curtailed to balance the power supply and load demand. Nevertheless, depending on the curtailment control strategy for wind power plants (WPPs), while the total amount of output power curtailment remains the same, the overall stored inertial energy within WPPs may vary. Furthermore, this stored inertial energy within WPPs can be used during disturbances to enhance frequency stability. This paper proposes a novel curtailment control strategy based on the sequential quadratic programming (SQP) optimization algorithm to effectively curtail WPPs and increase the overall stored inertial energy within WPPs. Then, the proposed solution can enhance frequency stability by providing a more inertial response from WPPs during disturbances. To verify the effectiveness of this novel curtailment control strategy, several case studies are conducted using the IEEE 39-bus system. The results show that the proposed curtailment control strategy effectively increases the stored inertial energy within WPPs while satisfying the required output power curtailment.

Management of renewable-based multi-energy microgrids with energy storage and integrated electric vehicles considering uncertainties

One of the best solutions to overcome environmental, technical as well as economic problems in the power system is the use of Plug-in hybrid electric vehicles (PHEVs). The penetration of a large number of PHEVs in the power system and the possibility of their proper control and management provide benefits of a large energy storage system for the distribution system operator. However, the correct management of PHEVs along with renewable energy sources (RESs) is a very important challenge that requires more research in this field. The main goal of the article is the scheduling of a microgrid with several PHEVs and RESs in order to achieve economic, technical and environmental benefits. To obtain more accurate results in microgrid operation, the intermittent behavior of renewable resources, PHEVs and loads has been modeled using the Mont Carlo simulation (MCS). Uncertainty parameters considered in this article include the charging demand of PHEVs, loads, electricity price and output power of RESs. The objective function is to reach minimum total costs considering the technical constraints. In order to resolve the defined optimization problem, including the objective function and the constraints of the problem, the modified sparrow search (MSS) algorithm is applied. The recommended technique is simulated on the test network with the MATLAB software and the outcomes are compared with conventional algorithms. As the results of the simulations show the suggested scheme performs superior performance than other optimization algorithms.

Optimal operation of active distribution networks hosting hybrid hydrogen‐electricity refuelling stations considering water demand under a stochastic‐IGDT approach

AbstractThis paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system.

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type-II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system.

Optimal sizing of hybrid energy storage system considering power smoothing and transient frequency regulation

The increasing integration of renewable energy sources (RESs) poses challenges of active power balance in both the normal operating states and contingencies. The hybrid energy storage system (HESS) consisting of the battery and supercapacitor is flexible, and can provide additional regulation capability. This paper proposes an optimal sizing scheme for the HESS considering power smoothing in steady-state operation and transient frequency regulation after disturbances. First, the output power of RESs is decomposed by the discrete wavelet transform, and the low- and high-frequency fluctuations are separately allocated to the battery and supercapacitor for smoothing. Then, an extended system frequency response model is developed for estimating the required transient frequency regulation capability (TFRC) of the HESS to arrest frequency excursion in contingencies. Furthermore, taking into account the fluctuation smoothing, required TFRC and investment cost, a multi-objective optimization model is established to optimize the size of the HESS. Additionally, the linear weighted method and generalized Benders decomposition are utilized to divide the optimization model into two stages, where a master problem and two subproblems are solved iteratively. A case study is conducted to validate the proposed scheme, showing high cost efficiency, and superior performance in smoothing power fluctuation and improving frequency nadir.

Robustly coordinated operational scheduling of a grid‐connected seaport microgrid under uncertainties

The modern seaport system is becoming more electrified by integrating energy storage system, offshore renewable energy sources (RESs), and shore-to-ship power supply system. The energy management and operational scheduling aim to maximise the profit gain by transacting energy with the main grid, reduce the operation cost while minimising the emission at seaport territory. This work proposes a robustly coordinated operational scheduling method of a seaport microgrid as a grid-integrated energy hub under uncertain renewable energy sources power output and load demand. In the day-ahead operation planning, generators, unit commitment and shore-to-ship load schedules are optimised. To address the uncertainty in the renewable energy sources, the day-ahead operation planning is modelled as a two-stage robust optimisation model with conflicting objectives and solved with the column and constraints generation algorithm. These results are used as input parameters for the hour-ahead generation scheduling in the following day. With uncertainty realisation, a shorter time horizon with higher time resolution deterministic optimisation is carried out for the generation dispatch. The proposed method is verified with the IEEE-33 bus distribution network with different uncertainties sets. The simulation results have verified the robustness and effectiveness of the method in dealing with uncertainties. Coordination between day-ahead and hour-ahead not only improves the accuracy and performance of the final dispatch result but also meets the distribution network constraints at generation and load buses.

Frequency Security Constrained Energy Management in an Isolated Power System

Short-term frequency instability is one of the major challenges that arise due to large-scale penetration of converter interfaced renewable energy sources (RESs) in isolated power systems. The short-term frequency instability is associated with high values of rate of change of frequency (RoCoF) and larger frequency excursions. In this paper, a frequency constrained energy management algorithm is proposed. The proposed algorithm manages the power output of RESs, energy storage system, and electric vehicles (EVs) in such a way that the system keeps nonsynchronous power reserve for providing virtual inertia support to avoid high values of RoCoF and frequency excursions. The non-synchronous power reserve is readily available and when deployed can limit the RoCoF as per the required standard. The proposed management algorithm selects the non-synchronous power reserve from different resources, i.e., RES (solar PV, wind turbine), energy storage, and EV, depending on the availability and system loading conditions. Simulation results show the effectiveness of the proposed algorithm.

IGDT-Based Robust Optimization for Multicarrier Energy System Management

In this paper, the information-gap decision theory (IGDT)-based robust problem model for multicarrier energy system (MES) management is presented. It is noted that in the proposed problem, electrical and gas energies are inputs of MES or energy hub, and electrical and heating energies are outputs of MES. The output electrical energy of hub provided by renewable energy sources (RESs), storage system, combined heat and power (CHP) system is managed by hub manager, and they do not participate in the market and input electrical energy. Also, it is considered that input electrical energy is bought from the PoolCo and bilateral contract markets. Moreover, the CHP is only connecting element for different energies. Therefore, the deterministic problem model expresses in the first step, where its objective function is minimized of hubs energy cost. Also, the constraints of problem are power flow equations and indexes limit in electrical, natural gas and district heating networks, PoolCo and bilateral contract market models, RES, storage system and CHP equations. This problem is as mixed integer nonlinear programming, where in the next step, the equivalent mixed integer linear programming model is presented because this new model can obtain the global optimal solution with low calculation error and time. In addition, the different loads, energy price in PoolCo market and RESs output power are uncertain parameters. Hence, this paper for considering uncertainty parameters presents the IGDT-based robust optimization for proposed problem. Finally, this problem is applied to standard test network with GAMS software, and thus the capability of the proposed problem investigates.

MPC-based frequency control strategy with a dynamic energy interaction scheme for the grid-connected microgrid system

In a microgrid system (MGS), effective frequency control strategies are extremely significant for maintaining the system frequency stabilization. Therefore, a model predicted control (MPC) method is presented to regulate the frequency fluctuation of the grid-connected microgrid system (GCMGS). The primary frequency control for the diesel generator (DG) and the secondary frequency control with the (MPC) for the energy storage (ES) are proposed in this paper. Simultaneously, in order to mitigate the power fluctuation caused by the load demand side and the renewable energy sources, a dynamic energy interaction scheme (DEIS) is designed. The power exchange between the MGS and utility grid can be achieved. Due to the randomness of the renewable energy sources, the system frequency is hard to remain stable. Thus, a constant constraint renewable energy sources power output is utilized to reduce the uncertainty of the renewable energy sources. Finally, several comparison experiments have been executed to illustrate the availability of the proposed control strategy.

Grid-connected energy hubs in the coordinated multi-energy management based on day-ahead market framework

This paper presents the coordinated energy hubs energy management in different networks based on the cooperation of hubs in day-ahead (DA) markets. The proposed model connects the electrical, natural gas and district heating networks by considering the electrical, natural gas as the hub's input, and electrical and district heating energies for the output of hubs. In this study, combined heat and power (CHP) and electrical storage systems, boiler, electric vehicles (EVs) parking lot and renewable energy sources (RESs) are considered in the context of energy hubs. In the process, the linear coordinated energy management problem includes a single linear objective function that is aimed at maximizing hubs profit in the DA market while considering linear networks and hubs constraints. Moreover, this model is taken into account uncertainty in different loads, DA market energy price, RES output power, and EVs parameters. Hence, the proposed problem is formulated as a scenario-based stochastic model using Monte Carlo simulation (MCS) for scenario generating and the fast backward/forward scenario reduction method for scenario reducing. Finally, this model is applied to a test system in the GAMS software environment, and the capacity and validity of proposed hubs energy management are investigated. Finally, simulation results confirm the efficiency of the hub coordinated energy management in the DA market.

Strategically incorporation of RES and DSTATCOM for techno‐economic‐environmental benefits using search space reduction‐based ICSA

In this study, an improved crow search algorithm (ICSA) is proposed for the optimal allocation of renewable energy sources (RESs) and distributed static compensator in a practical distribution network (PDN) to improve system voltage, reduce line losses, maximise economic benefit, and decrease pollutants’ emission. To improve the performances, the control parameters of ICSA are logically tuned to make them adaptive with the iteration. Voltage stability index is used to identify healthier and weaker buses (HBs and WBs) of the system and comparative performance analysis is conducted after placing the devices at the identified HBs and WBs. The proposed technique is applied for both the cases of reduced search space (only WBs) and full search space (all the load buses) to establish that the optimal placement is always obtained at the WBs of the network. The optimal allocation for each type of device is conducted considering the time-varying characteristics of load demand and RES's power output, and their optimal performances are analysed in details, which will be helpful for the distribution companies to identify the most suitable device for incorporating in PDN. Furthermore, to show the effectiveness, the proposed algorithm is also compared with other algorithms.

Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid

Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm where ESS power inputs are optimally determined to minimize the microgrid (MG) operation cost. The proposed algorithm consists of two stages. In the first stage, hourly schedules during a day are optimized one day in advance with the objective of minimizing the operating cost. In the second stage, the optimal schedule obtained from the first stage is repeatedly updated every 5 min during the day of operation to compensate for the uncertainties in load demand and RES output power. The ESS model is developed considering operating efficiencies and then incorporated in mixed integer linear programming (MILP). Penalty functions are also considered to acquire feasible optimal solutions even under large forecasting errors in RES generation and load variation. The proposed algorithm is verified in a campus MG, implemented using ESSs and photovoltaic (PV) arrays. The field test results are obtained using open-source software and then compared with those acquired using commercial software.

Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating

This paper addresses the optimal operation and scheduling of reconfigurable microgrids incorporating the dynamic line rating limitations during the islanded and grid-connected mode operations. The incorporation of the dynamic line rating of overhead feeders can potentially improve the system security when providing economical and technical benefits for the microgrid. The proposed framework takes into account a realistic formulation to minimize the total microgrid costs in both grid-connected and multiperiod islanded modes. Also, a stochastic framework based on unscented transform is proposed to model the uncertainties associated with renewable energy sources output power, market energy price, and load demand, as well as the weather uncertain parameters such as solar radiation, wind speed, and ambient temperature. Due to the high nonlinearity and complexity of the proposed problem, an efficient optimization algorithm based on the collective decision optimization algorithm is proposed. A new two-stage modification method is also developed to improve the algorithm search ability and avoid premature convergence. The proposed problem is examined on the IEEE 32-bus microgrid test system. The simulation results show the effectiveness of the proposed model and verify its economic and reliability merits.

Offering Model for Optimization of Virtual Power Plant Operation in Electricity Market

Distributed Energy Sources based on renewable generation have an important place in power systems due to liberalization of electricity markets and increase of environmental problems. The involvement of renewable energy sources (RES) in the energy market poses a serious challenge for RES owners. The main reason for this is the uncertainty about the power output of RES. For example, the power output of wind power plants (WPP) depends on the wind speed and the power output of solar power plants (SPS) depends on the solar radiation and clouds. This brings the risk of not meeting long-term or medium-term electricity delivery contracts. To remove this risk, a single-acting unit in electricity market is formed by combining different types of renewable and non-renewable generation units and storage systems. This unit is defined as Virtual Power Plants (VPP). In this study, an hourly operation scheduling is modeled to get maximum profit for VPP owner participating in day ahead market by saling or purchasing electricity. The VPP in the study consists of wind power plant, conventional power plant and energy storage system. The problem is formulated as a mixed-integer non-linear problem, applied for 24-hours time horizon and tested in GAMS software. The applicability of the method has been demonstrated by showing that the proposed method helps VPP owner about how to provide optimum sale/purchase bids in the day ahead market.

Islanding-Aware Robust Energy Management for Microgrids

With the current trend of transforming a centralized power system into a decentralized one for efficiency, reliability, and environmental reasons, the concept of a microgrid that integrates a variety of distributed energy resources into distribution networks is gaining popularity. In this paper, we investigate the energy management of a microgrid with renewable energy sources (RESs), such as wind turbines and solar panels, and co-generation in both grid-connected and islanded modes. To address the uncertainties associated with RES power output and grid-connection condition in the microgrid, we propose a two-stage adaptive robust optimization approach to provide a robust unit commitment schedule for co-generation units, with the objective of minimizing the total operating cost under the worst-case scenario of renewable power output and grid-connection condition. The proposed approach ensures robust microgrid operation in consideration of worst-case scenarios, and the over-conservatism can also be mitigated by introducing “budget of uncertainty” parameters. Finally, a case study is conducted to show the effectiveness of the proposed approach.

Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management

The operator of renewable-based microgrid (MG) tries to supply the local load with the lowest cost from the alternative energy sources containing upstream grid, micro-turbines (MTs), renewable energy sources (RESs) (photovoltaic (PV) systems and wind-turbines (WT)) and energy storage system (ESS). To purchase power from upstream grid, the optimal bidding curve of MG should be prepared to bid the market operator. Therefore, this paper proposes an information gap decision theory (IGDT) to obtain the bidding strategy of MG. IGDT includes the robustness and opportunity functions for upstream grid price uncertainty modeling. MG can consider the robustness decision (risk-averse) or the opportunity decision (risk-taking) under uncertainty environment. Also, the operator of MG uses the demand response program (DRP) which purpose is to reduce the energy procurement cost. Meanwhile, the proposed stochastic model considers the uncertainty modeling of local load and RESs output power by using a scenario stochastic model. To show the capability of proposed approach, two cases considering without and with DRP are studied. Beneficial results of DRP are utilized in case B, which the operation cost in case B is% 4.6 less than case A.

Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation

The stochastic energy procurement problem (SEPP) of large electricity consumer (LEC) with multiple energy procurement sources (EPSs) is proposed in this study. Also, the effects of demand response program (DRP) and energy storage system (ESS) on SEPP are studied in this work. The EPSs contain renewable energy sources (RESs), which include photovoltaic (PV) system and wind-turbine (WT), power market (PM), bilateral contracts and self-generation units. Also, the ESS (like as battery) and DRP are integrated in the SEPP by decision-maker of LEC to reduce the expected energy procurement cost. In the SEPP, the uncertainty models of market price, load and output power of RES (PV and WT) are considered. The normal distribution is used to generate scenarios for price, load and errors of temperature and insulation for PV output power uncertainty modelling. Also, the Weibull distribution is used to generate scenarios for wind speed for WT output power uncertainty modelling. Furthermore, the fast-forward selection based on Kantorovich distance approach is used for scenarios reduction. Actually, a case study is used to illustrate the capability and efficiency of the proposed SEPP.