Uncertainty Of Renewable Energy Sources Research Articles

Stochastic optimization in multi-energy hub system operation considering solar energy resource and demand response

The optimal operation of multi-energy systems based on energy hubs (EHs) is a significant challenge for operators due to the mutual impact of different energies and the uncertainty of renewable energy sources, electricity prices, the energy demand of consumers. To address this challenge, a stochastic scheduling framework for multi-energy hub systems is proposed in this paper to simultaneously optimize both EHs and distribution networks. The proposed framework considers RESs, uncertain parameters, DRPs, and emission to ensure system operation in fact. Furthermore, the uncertainty of electrical, heating, and cooling demands, electrical prices, and output power of RESs is modeled by probability density functions. They are divided into states by clustering technique to form the scenario matrix. A scenario reduction technique is then employed to reduce the number of scenarios to 10 to reduce the computational burden. The objective of the proposed framework is to minimize the total operation cost of the system, which is modeled as a mixed-integer nonlinear programming (MINLP) problem. The numerical results from different case studies show that the operation costs of the multi-energy systems using the proposed method are reduced by 2.0–14.5% among different analyzed cases. Additionally, CO2 emission is reduced by 1.9% and 22.3% considering DRPs and RESs, respectively.

Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

The international community has set ambitious targets to replace the use of fossil fuels for electricity generation with renewable energy sources. The use of large-scale (e.g., solar farms) and small-scale solutions (e.g., onsite green technologies) represents one way to achieve these goals. This paper presents a mathematical optimization framework to coordinate the energy decisions between the distribution network and the networked microgrids embedded within it. Utility-scale renewable and conventional generators are considered in the distribution network, while the microgrids include onsite renewable generation and energy storage. The distribution network operator utilizes demand-side management policies to improve the network’s efficiency, and the microgrids operate under these programs by reducing their energy usage, scheduling the electricity usage under dynamic tariffs, and supplying energy to the grid. The uncertainty of renewable energy sources is addressed by robust optimization. The decisions of the distribution network and the microgrids are made independently, whereas the proposed collaboration scheme allows for the alignment of the systems’ objectives. A case study is analyzed to show the capability of the model to assess multiple configurations, eliminating the necessity of load shedding, and increasing the power supplied by the microgrids (22.3 MW) and the renewable energy share by up to 5.03%.

Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs

AbstractIn this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively.

An intelligent robust cascaded control scheme for renewable energy based microgrid

AbstractHandling the uncertainties in the utility grid and uncertainties in renewable energy sources is a main challenge to accomplish the modern grid code requirements. This article proposes a new control strategy based on interval type‐2 fuzzy sets for controlling the grid‐connected dispersed generation (DG) system tied to the distorted electric grid. The proposed control technique can easily model and handle the uncertainties in the system parameters and renewable energy sources. The existence of three‐dimensional membership functions of type‐2 fuzzy sets offers an additional degree of freedom to counter the uncertainties in the system. The proposed strategy is effective in improving the dynamic response during system uncertainties such as distortions in grid voltage, variations in grid frequency, variations in renewable energy sources and due to presence of non‐linear and unbalanced loads. Also, it improves the quality of the current being injected to the grid during uncertainties. The proposed control strategy is applied to control the dc side capacitor voltage as well as to control the current loop of the grid connected inverter. The proposed system is simulated in MATLAB Simulink environment and the performance is compared with traditional controllers to show the effectiveness of the proposed control strategy during system abnormalities.

Simultaneous allocation ofEVcharging stations and renewable energy sources: An EliteRERNN‐m2MPAapproach

This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique. Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage deviation is considered as objective function to maintain a stable voltage profile in every nodes. The land cost is diminished with weightage maximization to serve maximal electric vehicle with minimal installation cost of electric vehicle charging station (EVCS). In this proposal, optimal location with capacity of fast charging stations and RESs are determined concurrently, while determined the deviation paths and uncertainties of RESs. To this intention, RERNN is proposed to cover the charging demand for EVs on the transportation network. Moreover, an m2MPA is proposed to optimal allocation of RESs and fast charging stations with respect to the distribution and transportation networks. Likewise, the proposed technique minimizes the adverse effects of fast charging demand on the grid, particularly during peak periods reducing the need for larger capabilities of energy storage units to absorb fast charging fluctuations and reduces the need for separate FCS installation. By then, the proposed technique is carried out in MATLAB/Simulink platform, its performance is compared with various existing techniques.

Clean Generation Mix Transition: Large-Scale Displacement of Fossil Fuel-Fired Units to Cut Emissions

To reduce greenhouse emissions and tackle anthropogenic climate change, more and more fossil fuel-fired units (FFUs) are being displaced by renewable generation units (RGUs). Due to the variability and uncertainty of renewable energy sources, generation expansion planning (GEP) that considers the large-scale displacement of FFUs (DFU) has become a great challenge. To realize a clean generation mix transition with a suitable and fairer multistage FFU phaseout schedule, this article proposes a distributionally robust GEP–DFU model, which explicitly coordinates large-scale DFU and the high penetration of RGUs. The Wasserstein moment metric and Benders decomposition are deployed to deal with the renewable generation uncertainty and reduce the computational complexity. Case studies on a practical provincial power system in China verify the effectiveness of the proposed approach and reveal how the system’s generation mix could be transformed into China’s first 100% renewable electricity supply paradigm by 2040.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Envisioning security control in renewable dominated power systems through stochastic multi-period AC security constrained optimal power flow

The accelerated penetration rate of renewable energy sources (RES) brings environmental benefits at the expense of increasing operation cost and undermining the satisfaction of N-1 security criterion in transmission systems. To address the latter issue, this paper envisions N-1 security control in RES dominated power systems through stochastic multi-period AC security constrained optimal power flow (SCOPF). The paper extends the state-of-the-art, i.e. deterministic and single time period AC SCOPF, to capture two new dimensions, RES stochasticity and multiple time periods, as well as emerging sources of flexibility such as flexible loads (FL) and energy storage systems (ESS). Accordingly, the paper proposes and solves for the first time a new problem formulation in the form of stochastic multi-period AC SCOPF (S-MP-SCOPF). The S-MP-SCOPF is formulated as a non-linear programming (NLP) problem. It computes optimal setpoints of flexibility resources and other conventional control means for congestion management and voltage control in day-ahead operation. As another salient feature, the proposed model is comprehensive and accurate, using: AC power flow model for both pre-contingency and post-contingency states, inter-temporal constraints for resources such as FL and ESS in a 24-hours time horizon and RES uncertainties. The importance and performance limitation of the proposed model through a direct approach, pushing the problem size up to the solver limit, are illustrated on two test systems of 5 nodes and 60 nodes, respectively. For the 60 node test system, the largest successful case can manage 30 scenarios and is solved in 22,110 s. Future work will develop a tractable solution algorithm.

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.

Improving transmission network using renewable resources efficiency by integrating pumped-storage hydroelectricity

Due to the increasing load demand, integrating renewable energy into the power system to satisfy the electrical consumption is the trend to develop the modern power system. However, the uncertainty of renewable energy sources affects the energy transmission efficiency and the reliability of power systems. To get this under control, connecting storage systems such as pump-storage hydroelectricity is seen to be an efficient solution. In this paper, Python language is used to describe input data and model the Vietnamese power system. The results show the feasibility of the hydro-storage model in improving the power system performance when renewable energy sources are involved.

A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.

Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, no enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e. g., system observ-ability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources, which have become a new operation mode with substantial advantages such as high flexibility, adaptability, and economy. This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources. The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments. In this regard, the faults of stochastic optimization and traditional robust optimization can be overcome. Firstly, a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed, and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets. Then, an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem, which improves the computational efficiency of the model. Finally, the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy. They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

Multi-Stage Real-Time Operation of a Multi-Energy Microgrid With Electrical and Thermal Energy Storage Assets: A Data-Driven MPC-ADP Approach

This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman’s equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.

Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation

Microgrids can effectively integrate renewable energy sources (RESs) and provide power for local customers. However, uncertainties of RESs and loads pose challenges to microgrid operation. The traditional point optimization method is unrealistic, and the widely used stochastic optimization (SO) method is time-consuming. Besides, battery energy storage systems (BESSs) are critical dispatchable devices to alleviate adverse effects of uncertainty, so an accurate nonlinear degradation cost model of BESSs should also be proposed. To handle such problems, the paper proposes an affine–arithmetic (AA)-based microgrid interval optimization (IO) method considering uncertainty and BESS degradation. First, the AA theory is introduced to model the RES and load variation ranges as intervals and calculate the interval uncertainty. Then, a nonlinear BESS degradation cost model is proposed, which can assess battery degradation costs considering different charging and discharging behaviors. The nondominated sorting genetic algorithm-II (NSGA-II) is employed to solve the proposed microgrid IO framework. For validation, the proposed IO method was compared with the point optimization method and SO method under various uncertainty realizations in a modified IEEE 33 bus system. The simulation results indicated the effectiveness of the proposed IO method in terms of an equilibrium between the simulation time and optimization performance.

Co‐optimization of active power curtailment, load shedding and spinning reserve deficits through hybrid approach: Comparison of electrochemical storage technologies

Under the constraints of fossil-fuel reserves depletion and climate change, the expansion of intermittent renewable generation creates a lot of power integration issues which undeniably disturb the overall system stability. Optimally planned, electricity storage systems are capable of managing the variability and uncertainty of renewable energy sources, guaranteeing power balance and ensuring feasible and economical operation. Here, the outcomes derived by a Genetic algorithm-driven priority list approach is provided, which effectively quantifies the impact of intermittent renewable energy sources on total production cost and the benefits of electricity storage. The experimental evaluation on three benchmark scenarios shows that cost improvements exist in terms of thermal generation improvement, lower renewable generation curtailment and load shedding avoidance cost. Zinc-air battery offers the highest net present value at relatively low PV penetration levels. Increased penetration levels favour Li-ion batteries followed by Pb-acid and Vanadium-redox flow batteries. In general, the viability of each storage device depends on the renewable penetration level, promoting the technologies with lower capital costs at limited shares, whereas at higher contribution frameworks systems with higher performance features become preferable.

Dynamic rolling horizon control approach for a university campus

An energy management system based on the rolling horizon control approach has been proposed for the grid-connected dynamic and stochastic microgrid of a university campus in Malta. The aims of the study are to minimize the fuel cost of the diesel generator, minimize the cost of power transfer between the main grid and the micro grid, and minimize the cost of deterioration of the battery to be able to provide optimum economic operation. Since uncertainty in renewable energy sources and load is inevitable, rolling horizon control in the stochastic framework is used to manage uncertainties in the energy management system problem. Both the deterministic and stochastic processes were studied to approve the effectiveness of the algorithm. Also, the results are compared with the Myopic and Mixed Integer Linear Programming algorithms. The results show that the life span of the battery and the associated economic savings are correlated with the SOC values.

Dual-layer optimized grid-connected operation strategy of electro-thermal multi-microgrid system considering the uncertainty of renewable energy sources

Interconnecting multiple combined heat and power (CHP) microgrids with the distribution network to form a CHP multi-microgrid system can promote the complementarity of various energy forms effectively and improve energy utilization efficiency. Therefore, a dual-layer stochastic optimal scheduling strategy of a multi-microgrid system interconnected with multiple CHP microgrids and multi-node distribution networks is proposed in this paper. In this strategy, the whole system is divided into two layers: (1) The lower layer adopts a stochastic simulation method based on versatile distribution to randomly generate a large number of scenarios and then uses a backward scenario reduction method to reduce the generated scenarios to a target number. Furthermore, with the goal of minimizing the sum of the weighted costs of the microgrid in each scenario, an economically optimal mixed integer programming model for a CHP microgrid with multiple energy storage devices is established. The output power of the electro-thermal units is optimized in each microgrid, and then the power to buy or sell power for each microgrid is determined. (2) The upper layer takes the minimum loss of the distribution system connected to multiple CHP microgrids as the goal and considers the power of each microgrid optimized by the lower layer as constraints. The optimal power flow model of the multi-node power distribution system is formulated based on the second-order cone relaxation technology, and the global optimal solution of the power node is obtained. Finally, the daily dispatch in which the IEEE-30 node power system connected to multiple CHP microgrids is used as an example for analysis. The results show that considering the uncertain factors of wind and solar output can effectively improve the reliability and robustness of the system when multiple CHP microgrids are connected to distribution network. Meanwhile, the coordinated use of multiple flexible coupling devices can reduce economic costs and promote energy consumption.

RETRACTED: Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources

A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the day-ahead prediction are considered as random variables for the upper-layer model. To realize the random variables at the lower layer, the revised model of energy storage and the demand response (DR) planning model are considered. Finally, the modified version of the artificial bee colony (ABC) algorithm is utilized to find the optimal solution. The improved ABC algorithm is a shape-memory method based on the collective intelligence and behavior of bees in a colony for finding the best nutrition source. In the improved ABC algorithm, with information exchange between the bees, based on Newton’s law of universal gravitation, the full potential of this algorithm is used to find the optimal solution given the constraints applied to the system. The proposed method is applied to a real system and the results show that the two-stage optimization algorithm and the proposed intelligent algorithm obtained the simultaneous optimization of different energy forms. The obtained numerical analysis results in test cases prove the following points: (1) The optimal synergistic supply of multiple energy forms has been provided based on the two-stage optimization algorithm and solution approach. (2) The surplus energy can be converted to natural gas by the power-to-gas converter (P2G) based on power cascade conversion in a multi-directional mode. (3) To get some revenue, the MEG is flexible enough to cooperate with the upper-grade energy network. (4) The DR-based price can smooth the load shape and increase the MEG operation revenue using some supplementary features. Also, P2G can sequentially develop the flexible multidirectional energy conversion in energy - gas - energy - cooling as a cascade. When the evaluated P2G energy rises by 450 kW, the total GST output raises by 1244 kWh. For more economic benefits, MEG can be connected to the upstream energy grid. Load management also increases the net revenue of the system.

Analysis of Hybrid Grid-Connected Renewable Power Generation for Sustainable Electricity Supply in Sierra Leone

The provision of electricity in a reliable and sustainable manner in provincial towns and villages in the small West Africa state of Sierra Leone requires the adoption of appropriate technologies. The rapid increase in electricity demand has generated great interest in how to tackle a possible long-lasting energy deficiency in the country. This paper aims at analyzing the techno-economic feasibility of a hybrid renewable energy system (HRES) for the sustainable rural electrification of Lungi Town, Port Loko District, Sierra Leone. Optimization, economic, reliability, and sustainability analyses were carried out using a genetic algorithm (GA), with the main objectives of minimizing the loss of power supply probability (LPSP) and cost of energy (COE). Three different case scenarios were configured, using a diesel generator (DG), wind/PV/DG/battery, and wind/PV/battery. Various combinations of these case scenarios were compared to determine which option was the most economically viable. In order to determine the case scenario with the lowest LPSP and COE, the operations and maintenance costs of the three cases were calculated. Using only DG for case one, the operations and maintenance cost amounted to USD 1050,348.12/year. The operation and maintenance cost for case two, which included wind/PV/DG/battery, was found to be USD 561,674.06/year. The operations and maintenance cost for case three, which included wind/PV/battery, was found to be USD 36,000/year. In standalone microgrids, however, the use of renewable energy sources is not reliable due to the uncertainty of renewable energy sources. Consequently, the simulation results show that the wind/PV/DG/battery-based HRES is the most cost-effective, reliable, and sustainable for the specific location in comparison to the current traditional method of electricity generation. Since there is abundant solar radiation with substantial wind speeds across the country, this HRES can be applied in most rural and remote areas in place of the current diesel generators (DGs) that are widely deployed in the country.