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The incorporation of renewable energy sources in isolated power systems is being significantly slower than in well-connected power systems. The intermittency and uncertainty of the power output of most renewable power technologies prevent a greater usage of these technologies in isolated power systems, in which the supply security is the major concern. In this paper we formulate a stochastic unit commitment problem that allows the participation of electric vehicles in energy, reserve capacity and primary frequency response markets in order to increase the flexibility of the power system operation. We explicitly consider the uncertainty in the power demand and renewable power availability, as well as accounting for the possibilities of contingencies of generating units using a N-1 security criterion. The proposed formulation is tested on an actual isolated power system comprising 38 generating units and 8 buses.

The power-to-gas (P2G) technology—a promising way to connect power and gas networks—will be widely used in the power-gas-heating integrated system (PGHIS) to promote its operational flexibility. However, the higher operational costs will affect the application of P2G on PGHIS scheduling, which cannot be ignored. In this paper, a PGHIS scheduling model considering the P2G cost and wind power utilization is proposed to analyze the impact of the P2G cost characteristics. First, a PGHIS including P2G is introduced, and different kinds of P2G operational costs are clarified to capture the cost characteristics of the P2G. Then, a multi-objective day-ahead scheduling model of the PGHIS is proposed to coordinate the tradeoff between its operational costs and the wind power utilization. Finally, the $\varepsilon $ -constraint and entropy weight methods are used to solve the multi-objective model. The numerical results demonstrate that P2G cost characteristics can significantly affect the day-ahead scheduling of the PGHIS, and the proposed model can effectively achieve a compromise solution.

A day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper for an electric vehicle (EV) battery swapping station (BSS), considering the dual uncertainties of swapping demand and photovoltaic (PV) generation. First of all, a BSS day-ahead scheduling model that can deal with the uncertainties is established by using the chance-constrained programming. The optimization objective is to minimize the cost of electricity purchased from the utility grid with the chance constraints of swapping demand satisfaction and the confidence level of the minimum cost. Then, the deterministic transformation of chance constraints is implemented based on probabilistic sequences of stochastic variables. Thereafter, the feasible solution space of the proposed model is determined based on the battery controllable load margin, and then the fast optimization method for the BSS day-ahead scheduling model is developed by combining the feasible solution space and genetic algorithm (GA). In order to evaluate the solution quality, a risk assessment method based on the probabilistic sequence for day-ahead scheduling solutions is proposed. Finally, the efficiency and applicability of the proposed method is verified through the comparative analysis on a PV-based BSS system. Results illustrate that the model can provides a more reasonable charging strategy for the BSS operators with different risk appetite.

The integration of large-scale stochastic renewable energy, the aging of transmission facilities, and the growth of load demand all contribute to the increasing congestion levels of transmission systems. Such factors pose considerable stress on the economical and secure operation of power systems and the accommodation of large-scale renewable energies. However, under the smart grid circumstance, some cutting-edge transmission technologies can bring potential cost-effective solutions to leverage the potential capacity of existing transmission infrastructures. Such technologies can help the utilities to deal with the rapid change of operating conditions of the power system in a more flexible manner. For example, the network topology optimization (NTO) technology can change the transmission topology based on the operating conditions, which increases the flexibility of the transmission system. Dynamic thermal rating (DTR) can evaluate the maximum transmission capacity of transmission lines dynamically according to the weather condition parameters around the conductor. These two cost-effective technologies are promising in improving the congestion mitigation performance and can contribute to the efficient utilization of transmission network-so they will bring potential economic and reliability benefits. This paper incorporates NTO and DTR in the network-constrained unit commitment (NCUC) framework to study their synergistic effect on the power system day-ahead schedule. Case studies are performed on a modified RTS-79 system. The numerical results verify that the coordination of NTO and DTR may help decrease the generation cost and wind power curtailment.

Due to their heat/cool storage characteristics, thermostatically controlled loads (TCLs) play an important role in demand response programmers. However, modeling of the heat/cool storage characteristic of large numbers of TCLs is not simple. In this paper, the heat exchange power is adopted to calculate the power instead of the average power, and the relationship between the heat exchange power and energy storage is considered to develop an equivalent storage model, based on which the time-varying power constraints and the energy storage constraints are developed, to establish the overall day-ahead scheduling model. Finally, the proposed scheduling method is verified using the simulation results of a six-bus system.

This paper develops an optimal simultaneous hourly reconfiguration and day-ahead scheduling framework in smart distribution systems considering the operation of the protection devices. The objective function of the model is defined as the minimization of system’s costs in terms of the costs associated with the purchased power from the wholesale market as well as the distributed generation (DG) owners, cost of switching actions, power losses cost and the cost for implementation of demand response (DR) programs. Moreover, a novel switching index along with the maximum number of switching actions based on the switch ages and critical branches in the network is presented. Due to the nonlinearity and non-convexity nature of the problem, the proposed optimization problem is then solved using a metaheuristic approach based on particle swarm optimization (PSO). As the result of the optimization process, the optimal set-points of DGs and responsive loads together with the optimal radial configuration of the distribution system for each hour of the scheduling time horizon are determined. To investigate the effect of DR programs and hourly reconfiguration on the load profile of the system, different price-based DR actions combined with interruptible load programs are also considered. Moreover, to demonstrate the satisfactory performance of the proposed model, the IEEE 33-bus distribution test system is thoroughly interrogated.

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.

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.

To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, CO 2 emission, imported electricity from grid and usage of local energy resources.

The high energy consumption of desalination systems represents a significant opportunity for the application of demand response schemes, particularly for the case of Non-Interconnected Island (NII) systems. In particular, the optimal management of the desalination demand can be proven quite beneficial in increasing the Renewable Energy Sources’ (RES) penetration, which is one of the main objectives in the day-ahead scheduling of the electricity system in Greek NIIs. This paper proposes a detailed representation of the desalination system, taking into account all the relevant constraints for the system’s operation. The mathematical representation of the aforementioned operation is incorporated in the day-ahead scheduling (DAS) for the case of Greek NII systems in order to define the optimal operational scheduling of a desalination system. The proposed optimisation procedure is applied for the desalination system installed in the Greek island of Kythnos. The results of the analysis indicate that the DAS problem shall be fully aware of the capabilities of the desalination system in order to allow specific water flows (in and out of specific reservoirs) at specific hours of the day, allowing the optimal exploitation of the available RES produced energy.

With the joint optimization of the electricity&ndash, gas&ndash, heating system (EGHS) attracting more and more attention, a distributed optimized scheduling framework for EGHS based on an improved alternating direction method of multipliers (ADMM) algorithm is put forward in this paper. The framework of the proposed algorithm is a co-ordinated convex distribution framework with inner and outer layers. The outer layer is a penalty convex&ndash, concave procedure (PCCP), the inner layer is an ADMM-FE (forced equality) procedure. In this framework, the outer layer optimization uses the convex and concave procedure to turn the non-convex airflow equation into a second-order cone constraint with successive iterations, and the inner layer ADMM-FE algorithm solves the convex model to obtain a convergent solution. In the end, we compare the established algorithm with the traditional ADMM algorithm and the centralized optimization algorithm through example simulation analysis, and the results verify the effectiveness of the proposed model and optimization algorithm framework.

In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.

Power generated by the natural gas (NG) is a promising option for solving the restrictions on the development of the power industry. Consequently, the high interdependence between NG network and electricity network should be considered in this integration. In this paper, a day-ahead scheduling framework of integrated electricity and NG system (IENG) is proposed at a distribution level based on the fast alternating direction multiplier method with restart algorithm considering demand side response and uncertainties. Within the proposed framework, the detailed model of the IENG system at a distribution level is established, where the NG flow equation is processed by incremental linearization method to improve the computational efficiency. The objective is to minimize the operation costs of the entire system. With consideration of the uncertainties of distributed generation and electricity load as well as the uncertainties from the NG load, a two-stage robust optimization model is introduced to obtain the worst case within the uncertainty set, which is solved by column and constraints generation algorithm. In addition, the demand-side response (DSR) model including the decentralized air conditioning (AC) load model and the centralized ice-storage AC load model is integrated into the scheduling framework. Finally, the proposed day-ahead scheduling framework is verified by numerical studies where the optimal scheduling schemes are obtained in different cases, both the effects of the uncertainties and the performance with introducing DSR to the system operation are analyzed. Published version

Nowadays, the hybrid wind&ndash, diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm&mdash, a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

In the new smart grid paradigm, the residential prosumers can more actively participate in the energy exchange mechanisms by adjusting their consumption through demand response programs and their own available local generation and energy storage system. On these bases, a new model of home energy management system (HEMS) is proposed and analyzed in this paper. Numerical studies show that the proposed HEMS is able to find the optimal operating scenario in different situations and to achieve a reduction of the billing costs by providing some electric flexibility to an aggregator or to a system operator.

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.

Multi-layer microgrids cluster (MLMC) has been formed in some certain areas with microgrids (MGs) merging into smart distributed system. It relies on an energy management system (EMS) to manage and schedule the optimal power flow among MGs and public power system in a stable and balanced way. Compared with EMS for single MG, energy management optimization for MLMC is much more complex that the interactions with other MGs require to be considered. In this paper a multi-time-scales EMS is proposed to minimize the total operating cost of MLMC. It consists of two parts: day-ahead static scheduling and real-time dynamic compensation. The day-ahead scheduling is an economic optimization based on the forecasting data within 24 hours. The real-time dynamic compensation is presented to compensate scheduling signal based on the real-time operating information, which is caused by the predictive error. Furthermore, charge-discharge protections of energy storage system (ESS) for each single MG are taken into account. A general-structure MLMG is studied as an example, and this problem is solved by mixed integer linear programming (MILP). The effectiveness of proposed EMS is verified in the MG Research Laboratory in Aalborg University, Denmark. The hardware-in-loop simulation results show the proposed EMS has a good convergence and performance in grid-connected mode.

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.

Microgrids are more and more called to satisfy, through the management of distributed generation sources and the electricity network, the demand for energy by local users. The simultaneous production of electrical and thermal energy by means of Combined Heat and Power (CHP) systems represents one of the features of a Microgrid and can contribute to improve system reliability, efficiency and economic performance. In this paper, an optimization procedure for day-ahead scheduling of a CHP-based Microgrid is developed, aiming to minimize operation and emission costs of Microgrid components in the presence of electric and thermal loads and renewable forecasts. To this purpose, four different operating strategies for CHP are accounted in Microgrid framework. The proposed methodology is based on a non-linear optimization technique and it is applied to the determination of day ahead operation program, with 15-minutes time step, for realistic model of an experimental Microgrid.

High penetration of wind power increases the complexities in system planning and operation. How to determine the required amount of operating reserve becomes a major problem due to the uncertainty and fast fluctuation of wind generation. Most of existing schedule methods determine the amount of operating reserve in day-ahead optimal scheduling considering economic costs and reliability indexes. However, they are not completely accurate due to the day-ahead forecast error of wind power and loads. It is well known that the forecast error of wind power and loads decreases from long-term timescale to short-term timescale. Therefore, it is important to develop an optimal schedule of energy and operating reserve in both day-ahead and intra-day to minimize total system cost considering ever-changing forecasted wind power. The spinning reserve is solved by day-ahead schedule to deal with contingencies like outages of units and other instantaneous failures. It will not change in intra-day schedule. Regulating reserve is pre-scheduled in day-ahead schedule and later adjusted in intra-day schedule due to the reduction of forecast errors of loads and wind energy. Both of the reserves can be provided by generators and demand response. We illustrate our methods in a modified 4-bus 9-generator system. Analysis shows that the proposed methods can provide a useful reference for system operators to schedule day-ahead, intra-day plan of energy and operating reserve. The system fluctuation induced by accessing wind energy is restrained by progressively utilizing a series of optimal allocations of energy and operating reserve in thermal power plants to enhance the secure and economical system operation.

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