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In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system. [ABSTRACT FROM AUTHOR]

Wind energy integration into microgrids, has introduced new challenges to energy management systems because of its intermittent behaviour. Owing to continuous changes in generation and consumption, the energy price is volatile. This study proposes a new two‐stage stochastic framework for day‐ahead scheduling of microgrids. Uncertainties associated to wind power generation, real‐time electricity price and load demand are considered. Different scenarios are generated using autoregressive moving‐average method and then are reduced using fast‐forward technique. On the first stage, the microgrid master controller determines the procured energy from the day‐ahead market and commitment states of distributed energy resources (DERs). In the second stage, the purchased energy from the real‐time market and schedules of committed DERs are obtained. The problem is modelled using mixed‐integer linear programming approach and is solved via CPLEX® optimizer. Both grid‐connected and stand‐alone modes of operation are investigated. Despite of high operation cost in island mode, coordination of energy storage systems, incentive‐based and price‐based demand response (DR) programmes affect economy of microgrids. The framework is examined on a test microgrid. Results show that both of the releasing the microgrid master controller authority and DR resources result in significant saving in operating; especially in emergency conditions such as islanded mode. [ABSTRACT FROM AUTHOR]

The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples. [ABSTRACT FROM AUTHOR]

• Define and quantify hydropower flexibility in power regulation and frequency response. • Flexibilities are coordinated in day-ahead scheduling of hydro-wind hybrid power system. • Benefits and costs of hydropower providing flexibility. • Suggested schemes weighing benefits and costs under different capacity ratios. Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability. [ABSTRACT FROM AUTHOR]

With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR , the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm. [ABSTRACT FROM AUTHOR]

Coupling the electricity and heating systems introduce opportunities to handle the intermittent nature of an increasing share of renewable power generation. Coordination of units in the coupled systems requires careful static dispatch towards an optimization objective. As the uncertainties in renewable forecasting are reduced towards operation time, operators can perform dispatch numerous times to balance the determined mismatches. With higher shares of renewable, sudden changes in power generation due to changing weather can cause a power imbalance that disturbs the system frequency. To handle such hazards, dynamic dispatch in the form of frequency control is necessary. Recent literature in integrated electricity and heating systems (IEHS) is studied in this work to combine the static and dynamic dispatch of units. A two-stage stochastic day-ahead scheduling method combined with a model predictive control (MPC) -based real-time optimal operation method that allocates both following and regulating reserves is used as the static dispatch of the IEHS. The allocated reserves constrain the performance of the dynamic dispatch by limiting the control capacity. In this work, an MPC coordinated frequency control of small- and large-scale heat pumps is considered as the dynamic dispatch of the IEHS. The performance of the dynamic dispatch is evaluated through a real-time closed-loop simulation platform, where the system conditions of the static dispatch are used as the steady-state conditions. Results show that despite challenging communication conditions, the frequency control of small- and large-scale heat pumps improves frequency nadir conditions after a power imbalance. • Investigation of static and dynamic dispatch of heat pumps in integrated electricity and heating systems. • Establish closed-loop real-time demonstration platform for heat pump dynamic studies. • Challenges against implementation of advanced coordinated frequency control from heat pumps. • Day-ahead scheduling, real-time optimal operation, and frequency control of integrated electricity and heating system on Bornholm island. • Demonstration of coordinated frequency support from flexible heating in real-time simulation environment. [ABSTRACT FROM AUTHOR]

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES. [ABSTRACT FROM AUTHOR]

Increasing penetration of variable loads and renewable resources in smart distribution networks brings about great challenges to the conventional scheduling and operation due to the uncertain nature. This paper presents a novel uncertainty handling framework, based on the underlying idea of robust optimization approach, to portray the uncertainties of load demands and wind power productions over uncertainty sets. Accordingly, a tractable min–max–min cost model is proposed to find a robust optimal day-ahead scheduling of smart distribution network immunizing against the worst-case realization of uncertain variables. In addition, considering demand response programs as one of the important resources in the smart distribution network, participation of customers in both energy and reserve scheduling is explicitly formulated. As the proposed min–max–min cost model cannot be solved directly by commercial optimization packages, a decomposition algorithm is presented based on dual cutting planes to decouple the problem into a master problem and a sub-problem. The master problem finds the day-ahead scheduling, while the sub-problem determines the worst-case realization of uncertain variables within uncertainty sets. Computational results for the modified version of IEEE 33-bus distribution test network demonstrate the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper deals with a residential hybrid thermal/electrical grid-connected home energy system incorporating real data for the load demand. A day-ahead scheduling (DAS) algorithm for dispatching different resources has been developed in previous studies to determine the optimal operation scheduling for the distributed energy resources at each time interval so that the operational cost of a smart house is minimized. However, demand of houses may be changed in each hour and cannot be exactly predicted one day ahead. System complexity caused by nonlinear dynamics of the fuel cell, as a combined heat and power device, and battery charging and discharging time make it difficult to find the optimal operating point of the system by using the optimization algorithms quickly in online applications. In this paper, the demand forecast error is studied and a near-optimal dispatch strategy by using artificial neural network (ANN) is proposed for the residential energy system when the demand changes are known one hour ahead with respect to the predicted day-ahead values. The day-ahead and hour-ahead optimizations are combined and ANN training inputs are adjusted according to the problem such that the economic dispatch of different energy resources can be achieved by the proposed method compared with previous studies. Using the model of the fuel cell extracted from experimental measurement and real data for the load demand makes the results more applicable in real residential energy systems. [ABSTRACT FROM AUTHOR]

Scheduling of power and real-time optimization for three industrial cogeneration plants at one of Dow's Louisiana site is presented in this paper. A first principle mathematical model that includes mass and energy balances for gas turbines, heat recovery units, steam turbines, pressure relief valves and steam headers is used to formulate an optimization problem to recommend the best strategy to trade power. The model has detailed operational information that includes equipment status and control curves for different operating scenarios. The model can also accurately predict the effect of ambient temperature, thereby resulting in an optimal day-ahead schedule. Adjustment of power schedule is done in the real-time market 30 min prior to the hour and implementation of the dispatched power schedule is done using a model predictive controller. [ABSTRACT FROM AUTHOR]

• Generic quantification methodology for real-time flexibility proposed. • Characterization and modeling of flexibility demand and supply capacity presented. • Flexibility evaluation and indicators based on Monte Carlo simulations proposed. • Various future scenarios and control strategies for PEVs investigated. • Relationship between the planning and real-time flexibility investigated. The widespread use of fluctuating renewable electricity has introduced challenges in balancing power systems. This can be alleviated by using flexible resources on the demand side to provide ancillary services at various timescales (e.g., day-ahead planning or real-time operation). However, practitioners lack a unified framework for measuring energy flexibility in the real-time operation frame. This study considers a control structure for clustered commuter plug-in electric vehicles (PEVs) in an office building with both day-ahead scheduling and real-time dispatch and proposes a generic quantification framework for assessing the capability of PEVs to manage the uncertainties of photovoltaic output and inflexible building load. Our evaluation considered the flexibility shortage in terms of power, energy, and control strategy, and the Monte Carlo method was used for probabilistic analysis. We conducted a sensitivity analysis to identify the key factors determining real-time flexibility. The results suggest that 1.5–2 times the number of PEVs is required to meet the defined level of real-time flexibility rather than planning flexibility. Possible conflicts in flexibility enhancement at different timescales and directions were observed under various day-ahead scheduling strategies; prediction information can be used to mitigate conflicts in scheduling strategies and improve the overall control performance. [ABSTRACT FROM AUTHOR]

In this paper, the coordination of constrained electricity and natural gas infrastructures is considered for firming the variability of wind energy in electric power systems. The stochastic security-constrained unit commitment is applied for minimizing the expected operation cost in the day-ahead scheduling of power grid. The low cost and sustainable wind energy could substitute natural gas-fired units, which are constrained by fuel availability and emission. Also, the flexibility and quick ramping capability of natural gas units could firm the variability of wind energy. The electricity and natural gas network constraints are considered in the proposed model (referred to as EGTran) and Benders decomposition is adopted to check the natural gas network feasibility. The autoregressive moving average (ARMA) time-series model is used to simulate wind speed forecast errors in multiple Monte Carlo scenarios. Illustrative examples demonstrate the effectiveness of EGTran for firming the variable wind energy by coordinating the constrained electricity and natural gas delivery systems. [ABSTRACT FROM PUBLISHER]

This paper proposes a pool-based demand response exchange (DRX) model in which economic demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling. [ABSTRACT FROM PUBLISHER]

As renewable energy becomes more prevalent in transmission and distribution systems, it is vital to understand the uncertainty and variability that accompany these resources. Microgrids have the potential to mitigate the effects of resource uncertainty. With the ability to exist in either an islanded mode or maintain connections with the main-grid, a microgrid can increase reliability, defer T&D infrastructure and effectively utilize demand response. This study presents a co-optimization framework for a microgrid with solar photovoltaic generation, emergency generation, and transmission switching. Today, unit commitment (UC) models ensure reliability with deterministic criteria, which are either insufficient to ensure reliability or can degrade economic efficiency for a microgrid that has a large penetration of variable renewable resources. A stochastic mixed integer program for day-ahead UC is proposed to account for uncertainty inherent in PV generation. The model incorporates the ability to trade energy and ancillary services with the main-grid, including the designation of firm and non-firm imports, which captures the ability to allow for reserve sharing between the two systems. In order to manage the computational complexities, Benders’ decomposition is applied. The commitment schedule is validated with solar scenario analysis, i.e., Monte-Carlo simulations are conducted to test the proposed dispatch solution. [ABSTRACT FROM AUTHOR]

The current trend of electricity market deregulation ushers in increasingly dynamic electricity pricing schemes. The cost-optimal scheduling of industrial loads with accurate price forecasts is therefore important. However, results in the current literature suggest that mean absolute percentage error (MAPE) is poor at indicating the economic benefit of a forecast. This paper presents the economic benefit analysis of electricity price forecast on the day-ahead scheduling of load-shifting industrial plants. A coal-conveying system with storage is used as a case study. The research uses three price forecasting methods on the PJM's market prices over a period of two years. Rank correlation (RC) between the predicted price and the actual price is proposed as an indicator of economic benefit. The results show that RC is a better indicator of economic benefit than root mean square error (RMSE) and MAPE. They also show that potential economic benefit obtainable from forecasts depends on price volatility and not mean price. An artificial forecast is used to validate the superiority of RC over MAPE and RMSE. It is observed that the predictability of a forecast's economic benefit is largely dependent on how responsive the load is to electricity price changes. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO's market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented using a 6-bus system and the IEEE-RTS, and several studies are conducted to demonstrate the merits of the proposed DR model. [ABSTRACT FROM AUTHOR]

This paper proposes a day-ahead stochastic scheduling model in electricity markets. The model considers hourly forecast errors of system loads and variable renewable sources as well as random outages of power system components. A chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling. Reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability. The chance-constrained stochastic programming formulation is converted into a linear deterministic problem and a decomposition-based method is utilized to solve the day-ahead scheduling problem. Numerical tests are performed and the results are analyzed for a modified 31-bus system and an IEEE 118-bus system. The results show the viability of the proposed formulation for the day-ahead stochastic scheduling. Comparative evaluations of the proposed chance-constrained method and the Monte Carlo simulation (MCS) method are presented in the paper. [ABSTRACT FROM PUBLISHER]

With the study of renewable energy power generation for microgrid becoming more and more significant, the microgrid's safe and economic operation has been paid more and more attention to. The microgrid economic operation cannot go well without perfect energy optimization scheduling, and making full use of energy storage units, coordinate microgrid internal power supply, storage and load, and the power/energy flow between the microgrid and the large power grid. Maximizing the microgrid operation efficiency under the premise of keeping balance between the supply and demand has become an urgent problem needing solution. Aiming at the problems in current literature, the dynamic optimization and static optimization models can't combine the day-ahead scheduling with real-time scheduling and fail to make full use of the storage and removal effect of energy storage units. In this paper, firstly, a multiple-time scale optimization scheduling scheme is put forward using the state of charge of energy storage unit as the bond between day-ahead scheduling and real-time scheduling. In day-ahead scheduling stage, a scheduling cycle is divided into 24 hours. In state of charge planning, the controllable micro power on-off plan and power generation plan are made based on the short power generation/consumption prediction data. In real-time scheduling stage, following the controllable micro power on-off planning and the state of charge of day-ahead scheduling, the future 5-15 minutes state of charge, charge and discharge power and the micro power supply power output planning is developed based on the real-time super short power generation/consumption prediction data. The day-ahead optimization model for the microgrid is also put forward, using the state of charge of energy storage units, controlled micro power supply power, and on-off state as control variables. The model puts four parts as constraint conditions within 24 hours, the controllable micro powers output, state of charge of energy storage unit, the interaction power between microgrid and power grid, and the power balance of microgrid. It takes the maximum utilization rate of renewable power generation, with minimum power supply cost of day as the goal. Then it takes the maximum utilization of renewable power, with minimum power supply cost of a day as the goal. It achieves the goal of maximum utilization of renewable energy power through meeting the demand of equivalent load, which simplifies the elements of the goal. To solve the multiple constraints, nonlinear, and mixed integer dynamic optimization models, it uses a genetic algorithm based on matrix real-coded which has strong global optimization ability, suited for parallel processing. Finally, an example of microgrid is given using the optimization model and algorithm which are proposed in this paper. The optimization scheduling results of parallel bidirectional power flow model and parallel unidirectional power flow model show that by reasonable control of state of charge in each period, the given optimization scheme can maximize utilization of renewable energy power and minimize power supply cost of microgrid whi.e at the same time achieving peak sharpening effect for the grid. [ABSTRACT FROM AUTHOR]

• A distributed method for DAS of IEGS is proposed. • The two-stage mixed-integer nonlinear DAS model can be effectively solved. • Limited information is shared between electricity and gas system operators. • A high-quality DAS solution can be obtained. • The security of IEGS against uncertainties can be strictly guaranteed. The urgency to address the uncertainty in the day-ahead scheduling (DAS) of integrated electricity and natural gas systems (IEGSs) has been highlighted, and the robust optimization has been proven to be an effective method. However, the gas system and electricity system are generally operated by different utilities in practice, i.e., gas system operator (GSO) and electricity system operator (ESO), which poses a considerable challenge to solve this two-stage mixed-integer nonlinear DAS model due to the limit on information sharing. In this paper, a novel distributed calculation method is proposed to solve the robust DAS model of IEGSs in a decentralized way. First, a mixed-integer master problem and a non-convex max–min subproblem can be obtained from the original DAS model by employing the column and constraint generation (CCG) method. Afterwards, based on the limited information exchange between ESO and GSO, an innovative decomposition method is proposed for the master problem to obtain the optimal DAS solution, and then a novel inner CCG algorithm is presented for the subproblem to guarantee the security of IEGSs. By doing so, the robust DAS of IEGS is decomposed into several mixed-integer linear programming (MILP) problems, and GSO and ESO can coordinately solve these MILPs with limited information sharing. Last, numerical tests on two IEGSs verify the superiority of the proposed method in hedging against the wind uncertainty and achieving the solution optimality. [ABSTRACT FROM AUTHOR]

Application of combined heat and power (CHP) units beside the distributed energy resources (DERs) persuades the power systems via the formation of multi-carrier microgrids (MGs). This paper presents the day-ahead scheduling of multi-carrier MGs. Renewable distributed generators are known as undeniable parts of modern power systems, which may increase the chanciness of the system. Information gap decision theory (IGDT) is applied to address the uncertainties of renewable sources. Moreover, a scenario-based stochastic approach is used to model the uncertainty of electricity prices in this method. Indeed a hybrid stochastic-IGDT-based optimization problem is proposed for optimal energy management of multi-carrier MGs. According to this method, the operation of multi-carrier MGs would be robust against uncertainties and contain a minimum profit. The proposed problem has been formulated as mixed-integer linear programming (MILP). Finally, the proposed energy management has been implemented into a sample multi-carrier microgrid. The results showed that how the MG operator's risk-averse character can change her/his decision-making. • Robust Optimal Scheduling of CHP-based Microgrids. • Proposing an IGDT-based approach to handle uncertainties of renewable generations. • Proposing a single level model for the IGDT method to obtain a global optimum. • Considering demand response programs for both thermal and electrical loads. [ABSTRACT FROM AUTHOR]

Abstract: Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the users’ requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method. [Copyright &y& Elsevier]

With the ever-increasing growth of electric vehicles (EV)s in the power industry, their significance as a flexible load has increased drastically. On the other hand, uncontrolled charging of these vehicles can cause serious problems in the grid, such as a peak in demand, a decrease in the life expectancy of transformers and as a result, an increase in charging costs of EVs for the EV owners. In this paper, a framework for day-ahead optimal charging of EVs is proposed which through optimization of active and reactive power exchange at each time interval, could prevent the problems mentioned above and at the same time increase the benefit of EV owners and network operators simultaneously. Furthermore, taking into account the effective factors on electrical energy consumption of EVs and the driving pattern of their owners, a route mapping algorithm is developed based on the proposed framework, so as to provide the EV owners with better services. The simulations are carried out using a hybrid interior-point optimization approach, based on traffic and geographic data collected from the city of Kowloon and a standard IEEE 33 bus system is used. The simulation results show that integrating optimal charging of EVs with a route mapping algorithm into the proposed framework can reduce the loss costs of the network during the hours of EVs' presence in the framework and the selling price of electricity to EV owners by 24.93% and 33.6%, respectively in comparison with the uncontrolled mode. Also, the average life expectancy of power transformers is increased by 2.97% in the optimal charging mode compared to the uncontrolled mode. • Route mapping for EVs is performed utilizing a cloud server and considering four impact factors. • Reactive power compensation is considered and subsequently, the loss costs of EVs during charging are reduced. • An optimal charging framework is proposed considering the simultaneous profit of EV owners and system operators. • The lifespan of EV batteries and power transformers are taken into account in the optimal charging framework. [ABSTRACT FROM AUTHOR]

• Proposing a generalized multi-energy producing-consuming self-consistent framework. • A rigorous mathematical paradigm is designed in the energy-coupled system. • Introducing multiple storage media under an ordinary scenario of offshore island. • The multi-energy community maximize the efficiency of clean energy utilization. The installed capacity of wind power and photovoltaic (PV) has grown up in recent years due to the requirement of developing green power. However, the curtailment of wind power and PV becomes serious as well owing to the relatively limited adjusting capability of the power grid, leading to tremendous waste of energy. Meanwhile, besides electricity, the requirement on other energy grows up gradually as well, resulting in remarkable pressure on the corresponding energy supply systems. On the other hand, the coupling and storage of multi-energy cannot only promote the consumption of renewable energy greatly but mitigate the pressure of various energy supply systems by replenishing various styles of energy. In this paper, taking offshore island as an example, a model of six-energy producing-consuming self-consistent (SEPCSC) system is put forward in the first. Further, by using the concept of energy hub, this model can be divided into supply side, conversion side and load side, each of which can be analyzed and modeled respectively. On this basis, a day-ahead dispatching model is established, which takes minimizing the total cost of purchasing energy from the external network as the objective function. Further, various appropriate constraints are designed correspondingly. The necessity of multi-energy coupling and introducing multiple storage media is validated with the simulation comparison to those cases of loose-coupling or battery only. In addition, the impact of various energy prices under the style of multi-energy coupling is investigated. It can be proven with case studies that the proposed SEPCSC system is capable of promoting the consumption of renewable energy, accompanied by the decrease in the total cost of system dispatching. [ABSTRACT FROM AUTHOR]

Renewable energy sources including wind farms and solar sites, have been rapidly integrated within power systems for economic and environmental reasons. Unfortunately, many renewable energy sources suffer from variability and uncertainty, which may jeopardize security and stability of the power system. To face this challenge, it is necessary to develop new methods to manage increasing supply-side uncertainty within operational strategies. In modern power system operations, the optimal power flow (OPF) is essential to all stages of the system operational horizon; underlying both day-ahead scheduling and real-time dispatch decisions. The dispatch levels determined are then implemented for the duration of the dispatch interval, with the expectation that frequency response and balancing reserves are sufficient to manage intra-interval deviations. To achieve more accurate generation schedules and better reliability with increasing renewable resources, the OPF must be solved faster and with better accuracy within continuous time intervals, in both day-ahead scheduling and real-time dispatch. To this end, we formulate a multi-period dispatch framework, that is, progressive period optimal power flow (PPOPF), which builds on an interval optimal power flow (IOPF), which leverages median and endpoints on the interval to develop coherent coordinations between day-ahead and real-time period optimal power flow (POPF). Simulation case studies on a practical PEGASE 13,659-bus transmission system in Europe have demonstrated implementation of the proposed PPOPF within multi-stage power system operations, resulting in zero dispatch error and violation compared with traditional OPF. [ABSTRACT FROM AUTHOR]

Distribution systems are transforming from passive networks to active ones with the development and deployment of renewable energy sources, microgrids (MGs) and virtual plants. Accompanied by the emergence of multiple stakeholders owning personal distributed generators (DGs), energy storage systems and even MGs, the optimal dispatch problem in active distribution networks (ADNs) is challenged. Basing on three-level day-ahead optimal scheduling framework, an up-to-down interaction mechanism is proposed firstly for the optimization in ADNs considering multi-stakeholders. The optimization mechanism starts from DN-layer with minimizing total active power loss of the DN, and then it implements the optimizations of all MGs within the optimizations of all users in the corresponding MG. The optimizing information of connection nodes will be fed back to upper layers in turn and this three-level optimization will be repeatedly implemented until the maximal unbalance power generation returned from all MGs satisfying the convergence condition. Additionally, an elasticity coefficient-based demand response program using time-of-use (TOU) pricing is integrated into the optimizations of MG-layer and User-Layer to guide peak load cutting. Moreover, a power flow rebalancing strategy is also integrated into the MG-layer optimization to accelerate the convergence of the whole solution. Finally, an actual 47-bus distribution system is employed to verify this proposed three-level optimization and the results show the effectiveness and applicability of the proposed method. • An Up-to-Down approach of three-level day-ahead scheduling mechanism for ADN is proposed. • Benefits of multi-stakeholders are considered in the proposed optimizing framework. • A rebalancing strategy is integrated into MG-layer optimization to accelerate convergence. • An elastic coefficient-based DR program using TOU pricing to guide peak load cutting. [ABSTRACT FROM AUTHOR]

• An effective three phases based EMS for optimal operation of two connected MGs. • Day-ahead unit commitment based on two stages for optimal scheduling of sources. • EMS optimization using a new advanced meta-heuristic algorithm, called GPSA. An efficient approach to optimal energy management and determine daily optimal operation schedule of two interconnected micro-grids (MGs), isolated from the grid, is proposed. The proposed energy management system consists of day-ahead and hour-ahead scheduling, and economic dispatch during real-time operation. The optimal day-ahead unit commitment can be achieved through two stages, management of the power generated from sources in both MGs and controllable load management. The day-ahead energy scheduling of each unit with technical constraints, being a complex nonlinear problem with several inequality constraints, needs to be optimized to achieve high quality operation as well as minimum daily forecasted energy consumption cost. Meta-heuristic algorithms seem more suitable to handle the task of optimal scheduling compared to the conventional analytic methods for power system economic dispatch. Considering the technical constraints, an advanced state-of-the-art meta-heuristic optimization technique, a modified version of the basic Porcellio Scaber algorithm (PSA) that offers improved efficiency in minimizing the objective function, is applied to solve the optimization problem. Results obtained demonstrate that the developed Global PSA is more efficient than a number of other meta-heuristic techniques to determine the optimal economic dispatch of multi-micro-grids incorporating various types of distributed generators. [ABSTRACT FROM AUTHOR]

• Proposing optimal day-ahead scheduling of MGs. • Considering renewable energy resources in the presence of Electric Vehicles. • Presenting Modified Shuffled Frog Leaping Algorithm considering Control Strategies. • Reporting superior solutions in scenario-based stochastic optimization. Microgrids (MGs) have turned into vital components of the modern power system with the capability of efficiently accommodating renewable energies and electric vehicles (EVs) with high flexibility. MGs can contribute to mitigating the operating cost and environmental emissions of power systems. Accordingly, the optimal operation of such systems is of very high significance. In this relation, the problem of optimal day-ahead scheduling of MGs is studied in this paper in the presence of renewable power generation, EVs, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem, characterized using the Monte-Carlo simulation (MCS) method. The developed framework includes one objective function, defined as the total operating cost minimization and the presented single-objective optimization problem is tackled using an effective optimization technique, named "modified shuffled frog leaping algorithm (MSFLA)". The suggested optimization framework takes into consideration various charging/discharging patterns of EVs. Finally, the problem is simulated on a test MG and the obtained results are compared to those derived by other algorithms to verify the performance of the MSFLA algorithm. [ABSTRACT FROM AUTHOR]