Uncertainties Of Load Demand Research Articles

Day-ahead interval optimization for CCHP system considering uncertainty of wind power and PV

To address the uncertainty of renewable energy and different load demands under different seasons in combined cooling, heating, and power (CCHP) system, a day-ahead interval scheduling optimization method is proposed for the CCHP system taking into account the uncertainty of wind power and photovoltaic (PV). Based on the interval theory, the uncertainty of wind power and PV are expressed by intervals. The total operating cost of carbon oxide emissions treatment cost and energy purchase cost is minimized as an objective function. A day-ahead interval optimization model of the CCHP system including wind power, PV, and energy storage is established considering different load demands in four seasons. The established nonlinear integer programming model is converted into the mixed-integer linear programming (MILP) model. The model is solved by interval linear programming. The CCHP system of a park is studied and compared with robust optimization. The simulation results verify the effectiveness of the method in this paper to achieve effective cooperative operation of the economy and environmental protection.

Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

There is a growing tendency for industrial consumers to invest in both photovoltaic (PV) and energy storage systems (ESSs) to meet their electricity requirements. However, the uncertainty of load demand and PV output brings great challenges for ESS operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESSs is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESSs, while the lower stage focuses on a more detailed minute-level operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, a chance constraint was introduced to achieve a win–win solution between PV power consumption and electricity tariff, while the terminal value constraint of the capacity of ESSs to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results showed that the proposed method can achieve an effective ESS energy management strategy.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

Resilience enhancement of integrated electricity-gas-heating networks through automatic switching in the presence of energy storage systems

Multi-carrier energy systems meet the needs of customers in different forms of energy. Given that these systems cover a large number of customers, it is very important to improve their resilience in emergency conditions. Hence, this paper presents a mixed-integer non-linear programming (MINLP) framework to enhance the resilience of multi-carrier energy systems integrated with electricity-gas-heating networks. In this regard, residential, commercial and industrial energy hubs with different load supply priorities, wind turbines and power to gas (P2G) technologies are considered in the model. In the proposed model, the system operator is capable to enhance system resilience under emergency conditions through automatic switching and implementing the direct load control (DLC) program. In this model, the scenario-based method is utilized to deal with load demand, wind speed, and solar radiation uncertainties. The proposed model is implemented on both medium-scale and large-scale systems and is solved by DECOPT solver in GAMS software. The results indicate that automatic switching reduces the load shedding by 84.17% during the outage of the line. The results also illustrate that the DLC program has reduced load shedding at high priority loads by about 66%. Finally, the high impact of storage systems and P2G technology on enhancing system resilience has been proven.

An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems

The large uncertainties in wind power generation will bring great challenges to the analysis of optimal reactive power dispatch (ORPD). This paper considers a multi-objective ORPD strategy solved by a heuristic search algorithm that combines the elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and a roulette wheel selection to optimize the operation of wind power integrated systems. The proposed ORPD strategy employs day-ahead predicted wind energy and load demand data to optimally set of the following control variables: i) optimal tap positions of on-load tap changers (OLTCs), ii) reactive demand set point of reactive power compensators and iii) active and reactive power outputs of wind farms (WFs) with the objectives to minimize: a) voltage deviations, b) active power loss, c) wind turbine harmonic distortions and d) number of switching operations of OLTCs. Because of the uncertainties of wind energy and load demand, hourly modifications of the day-ahead optimal results are also formulated to determine the real-time optimal reactive power dispatch. The proposed new ORPD strategy has been rigorously tested using IEEE 33-bus test system, PG&E 69-bus test system and modified real GB network. Results obtained confirmed the efficacy and applicability of the proposed strategy in both distribution and transmission networks.

Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties

With the advent of DC-powered renewable energy sources (RESs), the interest towards DC microgrid (DCmG) networks has gained attention recently. Integrating RESs has opened new challenges in handling the diversified application problems. The present work proposes an optimal operation strategy for droop-controlled islanded DCmGs considering the uncertainties involved in system variables along with the effect of correlation among them. A new point estimation technique, modified Gauss Quadrature based Point Estimate Method (GQ−PEM) is developed in this paper to model the uncertainties in load demand and solar generation. In this regard the mean and standard deviation errors of proposed method for 4-bus and 6-bus systems were minimum compared to the other existing techniques having errors of 0.00010082, 0.057165401 and 3.85333E−06, 0.059906462 respectively. Based on this a stochastic optimal power flow (SOPF) problem is formulated in DCmG environment with diversified objectives. The formulated SOPF problem is solved by new heuristic, dragonfly algorithm (DA), to obtain the optimal droop parameters for the modified 6-bus islanded DCmG test network. The suitable comparisons were made with other well-known heuristics; namely, the Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II), to validate the proposed approach. In addition to that, the effect of correlations was investigated with suitable NaTaf transformation (NaT) embedded within the proposed GQ−PEM. Various simulations pertaining to optimality and correlations were carried out to assess the robustness involved in the proposed approach.

Weekly bidding strategy and selling price determination for a energy internet zone

Energy internet zones, which supply and manage multiple energy kinds to customers simultaneously, attract more and more attentions for its high energy efficiency. This paper discussed the optimal weekly bidding strategy for a energy internet zone in midterm timescale. The optimal amount of bilateral contract and selling prices of multiple energy kinds can be obtained for expected profit maximization. Stochastic optimization is adopted to manage the uncertainties of electricity market prices and load demands. A energy internet zone in shanghai, China is adopted to illustrate the proposed model. The result shows that the energy internet zones prefer to supply the base load by signing bilateral contracts. The signed contracts increase with the decrease of contract prices and local photovoltaic system’s installed capacity.

Robust coordination of repair and dispatch resources for post-disaster service restoration of the distribution system

Since extreme weather events usually severely damage the power grid and lead to widespread power outages, coordinating repair and dispatch resources, such as repair crews (RCs), mobile power sources (MPSs), renewable energy sources (RESs) and energy storage systems (ESSs), is an efficient method to restore the power supply for outage loads. However, the uncertainties existing in RES output and load demand pose significant challenges to the service restoration of distribution systems in terms of security constraints. To address these uncertainties, this paper proposes a robust restoration approach to maximize the restored load in a distribution system by coordinating RCs, MPSs, RESs, ESSs and network reconfiguration. The coordination problem is formulated as a two-stage robust optimization model to generate optimal dispatching and operation schemes of multiple resources with guaranteed robustness against uncertainties. Involving some unexpected changes in topologies and distributed sources during post-disaster repair, the robust coordination model is re-optimized to guarantee the efficiency of repair and operation scheme. Simulation results show that the repair and dispatch resources can be coordinated effectively to accommodate the RES output and load demand uncertainties while maximizing the amount of restored load for service restoration of the distribution system.

A stochastic framework for secure reconfiguration of active distribution networks

Automatic reconfiguration is one of the key actions in self-healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance-constrained programming-based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks.

Differentiated local controls for distributed generators in active distribution networks considering cyber failures

The dependence of distribution network operation on cyber systems becomes heavier as the amount of grid connected distributed generation (DG) continually increases in the active distribution system (ADS). In order to reduce the operational risks under cyber failure scenarios, this paper proposes an optimization model to enable differentiated local control strategies for DGs with communication failures. First, the correspondence model between cyber failure scenarios and cyber link states is established based on the failure mode effect analysis (FMEA) method. Then a bi-level multi-objective optimization model of differentiated local control strategies for DGs is proposed, which considers the uncertainties of cyber failure scenarios and the uncertainties in power generations and load demands. It aims at minimizing the operational risks including voltage deviation and overcurrent (load curtailment) situations. Interval mathematics and intelligent optimization algorithm are adopted for solving the optimization problem. Finally, a test system is established to validate the effectiveness of the proposed local control strategies. Also, the influences of cyber system topologies on the control strategies are analyzed. This study is instrumental in devising effective operational strategies for DGs in ADS.

Multi-energy microgrid robust energy management with a novel decision-making strategy

Uncertainties in renewable energy sources and load demand have become a consequential issue which has led to a significant effect on the microgrid operation. In this paper, a novel cumulative relative regret decision-making strategy is proposed for the optimal energy management on a grid-connected multi-energy microgrid considering these uncertainties. The proposed strategy can ensure the robustness of the microgrid and reduce the conservatism of microgrid operation as compared with the traditional robust optimization method. Furthermore, the typical optimization model of microgrid energy management is improved by taking the demand response of the thermal load into account. The delay of heat transfer and the fuzziness of heating comfort are also investigated in order to obtain a more economic microgrid scheduling plan. Simulation results using different decision-making strategies are provided to verify the effectiveness of the proposed cumulative relative regret based robust optimization approach.

The robust recovery model of distribution network considering correlation between wind speed and load

In order to consider the correlation between distributed power output and load, this paper uses the value-at-risk theory to quantitatively calculate the uncertainty of wind speed and load demand, and establishes a wind speed-load joint probability distribution model using bivariate normal distribution, which will take into account the wind speed uncertainty. The robust decision-making problem of reliability and load participation is characterized as a mixed-integer linear programming problem, which takes the minimization of the equivalent load loss as the optimization goal, establishes the load recovery path as the optimization variable, and takes the wind speed and load as the disturbance variable to account for the wind speed-The load-dependent robust restoration model of the distribution network is solved by the Gurobi high-efficiency solver. Finally, the IEEE69-bus power distribution system with wind power was used to verify the effectiveness of this model and solution method.

Optimal Locating and Sizing of BESSs in Distribution Network Based on Multi-Objective Memetic Salp Swarm Algorithm

Battery energy storage systems (BESSs) are a key technology to accommodate the uncertainties of RESs and load demand. However, BESSs at an improper location and size may result in no-reasonable investment costs and even unsafe system operation. To realize the economic and reliable operation of BESSs in the distribution network (DN), this paper establishes a multi-objective optimization model for the optimal locating and sizing of BESSs, which aims at minimizing the total investment cost of BESSs, the power loss cost of DN and the power fluctuation of the grid connection point. Firstly, a multi-objective memetic salp swarm algorithm (MMSSA) was designed to derive a set of uniformly distributed non-dominated Pareto solutions of the BESSs allocation scheme, and accumulate them in a retention called a repository. Next, the best compromised Pareto solution was objectively selected from the repository via the ideal-point decision method (IPDM), where the best trade-off among different objectives was achieved. Finally, the effectiveness of the proposed algorithm was verified based on the extended IEEE 33-bus test system. Simulation results demonstrate that the proposed method not only effectively improves the economy of BESSs investment but also significantly reduces power loss and power fluctuation.

A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting

Accurate load forecasting is challenging due to the significant uncertainty of load demand. Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning with the decision-making ability of reinforcement learning, has obtained effective solutions to various optimization problems. However, no study has been reported, which used deep reinforcement learning for short-term load forecasting because of the difficulties in handling the high temporal correlation and high convergence instability. In this study, a novel asynchronous deep reinforcement learning model is proposed for short-term load forecasting by addressing the above difficulties. First, a new asynchronous deep deterministic policy gradient method is proposed to disrupt the temporal correlation of different samples to reduce the overestimation of the expected total discount reward of the agent. Further, a new adaptive early forecasting method is proposed to reduce the time cost of model training by adaptively judging the training situation of the agent. Moreover, a new reward incentive mechanism is proposed to stabilize the convergence of model training by taking into account the trend of agent actions at different time steps. The experimental results show that the proposed model achieves higher forecasting accuracy, less time cost, and more stable convergence compared with eleven baseline models.

Deep-learning- and reinforcement-learning-based profitable strategy of a grid-level energy storage system for the smart grid

A profitable operation strategy of an energy storage system (ESS) could play a pivotal role in the smart grid, balancing electricity supply with demand. Here, we propose an AI-based novel arbitrage strategy to maximize operating profit in the electricity market composed of a grid operator (GO), an ESS, and customers (CUs). This strategy, the buying and selling of electricity to profit from a price imbalance, can also cause a peak load shift from on-peak to off-peak, a win-win approach for both the ESS operator (EO) and the GO. Particularly, to maximize the EO's profit and further reduce the GO's on-peak power, we introduce a stimulus-integrated arbitrage algorithm, providing an additional reward to the EO from the GO with different weights for each peak period. The algorithm consists of two parts: the first is recurrent neural network-based deep learning for overcoming the future uncertainties of electricity prices and load demands. The second is reinforcement learning to derive the optimal charging or discharging policy considering the grid peak states, the EO's profit, and CUs’ load demand. We find it significant that the suggested approach increases operating profit 2.4 times and decreases the on-peak power of the GO by 30%.

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the ε-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS.

Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems

To comprehensively consider the impact of uncertainties of variable energy resources (VERs) and load demands on the scheduling plans, the paper proposes a multi-time scale coordinated optimization (MTSCO) method of the energy hub (EH) with multi-energy flows and multi-type energy storage systems (MESSs). The MTSCO involves with global optimization of day-ahead economic dispatch (DAED), local intra-day receding-horizon optimization (IDRHO) and real-time adjustment (RTA). DAED formulates the 24-period output of controllable devices integrated in EH through minimizing its operation cost of a single day. Following the formulated DAED plan, IDRHO minimizes the sum of energy purchase cost and MESSs’ penalty cost within every 15 min. Further, RTA provides real-time feedback to IDRHO and minimizes the total adjustment amount of all controllable devices every 5 min. The simulation results indicate that the proposed multi-time scale dispatching and MESSs enable to promote the EH economy, acquire better dispatching solution aiming at uncertainties of VERs and varying load demands and alleviate the grid’s compensation for fluctuation.

A stochastic multi-objective optimization framework for distribution feeder reconfiguration in the presence of renewable energy sources and energy storages

In this paper, a multi-objective optimization framework is proposed to solve the distribution feeder reconfiguration (DFR) problem and its operation considering the demand response (DR) program, renewable energy sources (RES's), and electrical energy storages (EES's). The proposed model is implemented on 33-bus and 118-bus radial distribution systems while the uncertainties of RES's output power, load demand and electricity price are taken into account. The Monte Carlo simulation approach is used to generate scenarios while the backward scenario reduction approach is used to reduce the number of scenarios. The studied problem is modeled using the Epsilon-constrained method as a two objective problem and it is solved in the form of five case studies using the GUROBI solver in GAMS software. Our analysis of the results shows that reducing losses and increasing system reliability increases the production of local generation units, thereby increasing the operating costs. In addition, simulation results demonstrate that considering the dynamic topology reduced losses by 9.73% and increased reliability by 4.7%. The results also show that using the DR program reduces LMP by about 20% during peak hour.

A novel trilinear deep residual network with self-adaptive Dropout method for short-term load forecasting

Short-term load forecasting underlies the effective energy management. However, accurate short-term load forecasting is a big challenge owing to the significant uncertainty and volatility of load demand. Deep learning has successfully been applied for short-term load forecasting, but may encounter the problems such as vanishing gradient, exploding gradient, and overfitting of the neural networks, which affect the robustness of the forecasting results. Herein, a novel deep residual network with self-adaptive Dropout method is proposed for short-term load forecasting. First, a new trilinear deep residual network with three routes of stacked residual blocks is proposed to solve the problems of vanishing gradient and exploding gradient of neural networks. Second, a new self-adaptive Dropout method that considers the effects of both redundant features and interference terms of the neural networks is proposed for automatically setting the neuron drop ratio so as to improve the robustness of the model. Finally, an improved neural network ensemble method is applied to further enhance forecasting accuracy without additional training costs. The proposed model has been proven to achieve better forecasting accuracy and robustness than state-of-the-art baseline models on two public datasets. Therefore, the proposed model is beneficial in promoting the development of practical smart grids.