Scheduling Of Energy Resources Research Articles

Ant colony optimization for feasible scheduling of step-controlled smart grid generation

The electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized energy resources will have to responsibly perform control tasks like frequency or voltage control. For many use cases, scheduling of energy resources is necessary. In the multi-dimensional discrete case–e.g., for step-controlled devices–this is an NP-hard problem if some sort of intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization, hence, becomes a difficult task. We prove the NP-hardness for the example of co-generation plants and demonstrate the multi-modality of systematically designing feasible solutions. For the example of day-ahead scheduling, a model-integrated solution based on ant colony optimization has already been proposed. By using a simulation model for deciding on feasible branches, artificial ants construct the feasible search graphs on demand. Thus, the exponential growth of the graph in this combinatorial problem is avoided. We present in this extended work additional insight into the complexity and structure of the underlying the feasibility landscape and additional simulation results.

Optimal scheduling of self-healing distribution systems considering distributed energy resource capacity withholding strategies

This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system’s energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively.

Energy Management System for Hybrid Microgrids

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

A Cost-Effective Optimization for Scheduling of Household Appliances and Energy Resources

In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers’ electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers’ electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches.

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Demand response (DR) is a flexible way to improve distributed energy resource scheduling. The innovative contribution of this paper is to include complex contracts in the model, which can accommodate the constraints according to the special expectations of each player. Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers. Multi-period DR events are considered. In this way, consumers can specify the limits on the time, power, and remuneration regarding participation in DR events, which has not been considered in the literature. The state of the art treats these aspects separately or uses a statistical approach, without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment. The model has been validated for 218 consumers using several scenarios and different types of distributed generation, showing that it is possible to increase DR with respect to the preferences of consumers.

Real-time scheduling of community microgrid

The community microgrid (CMG), powered by local energy resources and energy storage devices, is one of the most promising intelligent entities for the future smart grid. However, the scheduling of CMG energy resources for real-time energy management is a challenging optimization problem due to the variability of renewable generation and complex system constraints. In this work, a new three-layer real-time scheduling framework is proposed. The novelty of the proposed approach stems from the feedback mechanism introduced between the scheduling layers which enables the generation of cost-effective schedules. The proposed approach has better ability to handle the impacts of variations in renewable generations/demand within the CMG. To solve the real-time scheduling problem of CMG, a heuristic-based differential evolution (DE) algorithm is introduced. The significance of the developed heuristic is that it can generate feasible solutions with lower computational effort. The performance of the proposed algorithm is validated by solving benchmark problems and comparing the results with state-of-the-art algorithms. Finally, the developed scheduling framework is employed for real-time energy management of CMG. The experimental results confirm the efficacy of the proposed scheduling approach over the existing ones.

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory

The utilization of an Energy Management System (EMS) for the optimum scheduling of generation units, as well as demand side resources is essential due to the high penetration of Distributed Energy Resources (DERs) in microgrids (MGs), to achieve the desired objectives. As a result of the restructuring of the power systems and increasing the electricity prices during some periods in a day, demand side programs have been highly valuable by electricity customers. In this paper, a Demand Response (DR) model has been proposed to present the behavior of responsive controllable loads in response to the DR calls. Moreover, optimal scheduling of energy resources is developed for a typical MG by considering the presence of both electrical and thermal demands. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, demand response resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. Furthermore, the uncertainty resources of the wind speeds and electrical load are handled by the Information Gap Decision Theory (IGDT) method. The performance of the proposed framework is comprehensively analyzed on the IEEE 33-bus test system. The advantage of the proposed methodology under the uncertainty conditions is analyzed by the Monte-Carlo simulation method when the different realization of the wind power and electrical load are considered.

An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a modified krill herd algorithm

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named “modified krill herd (MKH)” algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method.

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

This article introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB Ⓡ Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

Optimal Scheduling of Residential Electricity Demand Based on the Power Management of Hybrid Energy Resources

Abstract The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of operation.

Optimal economic–emission power scheduling of RERs in MGs with uncertainty

This study proposes a framework for economic-emission dispatch problem (EEDP) in microgrids (MGs). The problem of EEDP aims at finding the optimal scheduling of renewable energy resources (RERs) in isolated and grid-connected MGs. The EEDP is formulated as a non-linear constrained multi-objective optimisation problem. It minimises the total cost and emission simultaneously. Simulation results in nine different cases are conducted to emulate the complexity of the EEDP. Also, the proposed scheduling procedure is tested under abnormal operation of MGs considering unexpected increase in power demand, shortage of battery storage and partial shedding of photovoltaic units. These cases reveal the capability of the proposed method. A multi-objective function is employed to increase the economic benefits and minimise emission issues. In this line, the simulation results are obtained using an enhanced moth-flame optimisation (EMFO) algorithm. The obtained results verified the effectiveness, robustness and global convergence for minimising emission and generation costs. These results are compared with several well-known techniques in the literature. Notable economic and environmental benefits are achieved using the EMFO that lead to flexible scheduling of RERs for MG operation under uncertainty.

Day-ahead Resource Scheduling in Distribution Networks with Presence of Electric Vehicles and Distributed Generation Units

In this paper a new framework for scheduling of available resources in the distribution networks is developed. In this respect attempts are focused on interactions between charging/discharging profiles of electric vehicles (EVs) and output power of distributed generation units. To reach this goal, the proposed framework is designed as a two-stage optimization procedure. In the first stage, the charging/discharging schedules of EVs are extracted running a linear programing optimization problem taking into account the EV users' constraints and requirements. The usage profiles of the DG units, strategy of buying electricity from the market and also the final charging/discharging patterns of the EVs are set running the second stage of this scheduling framework. The attained mixed-integer non-linear programing optimization problem is linearized and a procedure is organized to check the technical aspect of network. The results show that the proposed energy resource scheduling method satisfies financial and technical goals of network.

Day-Ahead Scheduling of a Local Energy Community: An Alternating Direction Method of Multipliers Approach

The paper focuses on the day-ahead operational planning of a grid-connected local energy community (LEC) consisting of an internal low-voltage network and several prosumers including generation units, battery storage systems, and local loads. In order to preserve, as much as possible, the confidentiality of the features of prosumers’ equipment and the production and load forecasts, the problem is addressed by designing a specific distributed procedure based on the alternating direction method of multipliers (ADMM). The distributed procedure calculates the scheduling of the available energy resources to limit the balancing action of the external grid and allocates the internal network losses to the various power transactions. Results obtained for various case studies are compared with those obtained by a centralized optimization approach. The results confirm that, in the considered LEC framework, each of the prosumers achieves a reduction in costs or increases revenues in case it participates to the LEC with respect to the case in which it can only transact with an external energy provider.

Hierarchical Energy Management System for Home Microgrids

With the spread of distributed energy resources, sensing infrastructure, and automation facilities, modern homes are becoming “home microgrids.” This paper intends to support this trend and proposes a two-stage hierarchical energy management system for smart homes by considering both day-ahead and actual operation stages. In the day-ahead stage, an efficient scenario analysis approach is developed to account for residential photovoltaic solar power uncertainty. The approach performs solar power scenario generation and reduction based on the Wasserstein distance metric and K-medoids, respectively. This is then followed by the use of a stochastic day-ahead residential energy resource scheduling model. In the actual operation stage, a semi-scenario based rolling horizon optimization mechanism is proposed, based on which an actual operation model is established. Simulations are then conducted to validate the effectiveness of the proposed system.

Coordinated residential energy resource scheduling with human thermal comfort modelling and renewable uncertainties

With development of two-way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air-conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K-medoids-based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level.

Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems

Improved performance electricity demand forecast can provide decentralized energy system operators, aggregators, managers, and other stakeholders with essential information for energy resource scheduling, demand response management, and energy market participation. Most previous methodologies have focused on predicting the aggregate amount of electricity demand at national or regional scale and disregarded the electricity demand for small-scale decentralized energy systems (buildings, energy communities, microgrids, local energy internets, etc.), which are emerging in the smart grid context. Furthermore, few research groups have performed attribute selection before training predictive models. This paper proposes a machine learning (ML)-based integrated feature selection approach to obtain the most relevant and nonredundant predictors for accurate short-term electricity demand forecasting in distributed energy systems. In the proposed approach, one of the ML tools- binary genetic algorithm (BGA) is applied for the feature selection process and Gaussian process regression (GPR) is used for measuring the fitness score of the features. In order to validate the effectiveness of the proposed approach, it is applied to various building energy systems located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other feature selection techniques. The proposed approach enhances the quality and efficiency of the predictor selection, with minimal chosen predictors to achieve improved prediction accuracy. It outperforms the other evaluated feature selection methods. Besides, a feedforward artificial neural network (FFANN) model is implemented to evaluate the forecast performance of the selected predictor subset. The model is trained using two-year hourly dataset and tested with another one-year hourly dataset. The obtained results verify that the FFANN forecast model based on the BGA-GPR FS selected training feature subset has achieved an annual MAPE of 1.96%, which is a very acceptable and promising value for electricity demand forecasting in small-scale decentralized energy systems.

Optimal Scheduling of Hybrid Energy Resources for a Smart Home

The present environmental and economic conditions call for the increased use of hybrid energy resources and, concurrently, recent developments in combined heat and power (CHP) systems enable their use at a domestic level. In this work, the optimal scheduling of electric and gas energy resources is achieved for a smart home (SH) which is equipped with a fuel cell-based micro-CHP system. The SH energy system has thermal and electrical loops that contain an auxiliary boiler, a battery energy storage system, and an electrical vehicle besides other typical loads. The optimal operational cost of the SH is achieved using the real coded genetic algorithm (RCGA) under various scenarios of utility tariff and availability of hybrid energy resources. The results compare different scenarios and point-out the conditions for economic operation of micro-CHP and hybrid energy systems for an SH.

An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids

Nowadays, renewable energy resources are increasingly used to supply electrical loads in micro grids, which these units should be scheduled coordinately. In this paper a stochastic model for coordinated scheduling of renewable and thermal units is proposed. Understudied units consists of fuel cell units with proton exchange membrane which generate heat and power simultaneously (PEMFC-CHP), wind and photovoltaic units. Moreover, the strategy of storing hydrogen is also considered for PEMFC-CHP units. Uncertainties of wind speed, solar radiation and market prices are considered using scenario based method. In the proposed stochastic programming problem, the strategy of storing hydrogen is considered by a mixed integer nonlinear programming (MINP) problem. The uncertainties of parameters convert the MINP problem to a stochastic MINP one. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective function. To solve this problem, Modified Teaching-Learning-Based Optimization (MTLBO) algorithm is used and its performance is evaluated on a modified 33 bus distribution network. Simulation results represent that by using MTLBO method, the revenue increases more than 5 percentages in comparison with other optimization methods. In addition, considering CHP increases total profit of the system more than 15%.

Distributed residential energy resource scheduling with renewable uncertainties

Advances in metering and two-way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method.