This study presents a multi-objective problem of the optimal power management of micro-grids, which is based on modified Tribe-PSO. Minimising total cost and emission over a 24 h time horizon are the objective functions in this problem. For the participation of consumers in the management of micro-grids, demand response program is used. The scheduling approach is tested on the IEEE standard distribution network with 33 buses and is compared in two modes: grid-connected and stand-alone. Simulation results show that the presented power and demand scheduling provide a lower operation cost and more efficient utilisation of the renewable energy sources and the demand response programs.

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context.

This study presents a demand response (DR) model to curtail the load during peak hours in a secondary (230/440 V) distribution system of Tamil Nadu Generation and Distribution Corporation (TANGEDCO), a state-owned enterprise, India. TANGEDCO penalises utilities if they violate their permitted contractual limit of demand. Conventional demand control usually curtails a specific region of the secondary distribution network. The limitation of the complete blackout of the particular region in the distribution network increases the loss of load probability. Hence, this project implements an economic DR model in real time using demand side forecasting and by scheduling air-conditioning loads based on their priority. The pilot project is executed and is monitored at the National Institute of Technology, Tiruchirappalli, India campus. A standard back propagation neural network is used for forecasting a 15 min interval ahead maximum demand in kVA. This economic model comprises of a communication network that uses ON/OFF switching wirelessly controlled relay modules. Finally, the benefits and the strategy involved in the project are presented. It is found that the proposed scheme prevents the electrical demand from exceeding the contractual limit, whereby the penalty due to the violation is zeroed when compared to the previous year.

In order to fully consume renewable energies and schedule demand-side resources more hierarchically, a double-deck optimal schedule model of micro-grid, which connects to power grids and concludes battery energy storage system (BESS), is proposed. Unlike the original peak-valley time-of-use (TOU) price, in the upper layer optimal schedule, the improved TOU price which takes account into the user's satisfaction can express the adjusted loads accurately and the resulting net loads can be treated as a link between upper and lower layer scheduling. For the reason of having no precise model of BESS, the lower model for the goal of minimising the operation cost is solved by action dependent heuristic dynamic programming algorithm that is not relying on the accurate controlled object model. This algorithm is used to obtain the most optimal performance index function and control strategy by the optimal iterative process, which is based on the back propagation neural network used for evaluating the optimal performance index. Analysis of examples and results has been presented to show the effectiveness of the proposed strategies.

Integration of wind generation and demand response (DR) poses challenges for the power system operation due to their uncertain characteristics. In this study, an economic dispatching method considering the uncertainties of wind and DR is proposed. The method based on the information gap decision theory (IGDT) can be used to evaluate risks associated with uncertainties from risk averse (RA) and risk-seeking (RS) perspectives. The RA IGDT-based model can provide maximum tolerable robustness region for the required cost target. The RS IGDT-based model can help achieve the lowest operation costs with desired uncertainties. The proposed model is bi-level. The upper level subproblem aims to maximise (minimise) the allowable uncertainty level to satisfy the pre-determined cost target, while the lower level subproblem is to maximise (minimise) possible cost considering the uncertainties. The bi-level model is then transformed into a single level mixed integer linear programming problem that can be solved through commercial solves. Finally, the authors evaluate the performance of the IGDT-based approach by simulations on the modified 6-bus and IEEE 118-bus systems. The results show that the proposed approach can provide suggestions for system operators to make appropriate scheduling plan based on the expected cost targets and risk preferences.

This article addresses a critical issue on the robust unit commitment (RUC), which is the construction of an accurate and reliable uncertainty set for solar photovoltaic (PV) systems. The classic robust unit commitment (CRUC) provides an inaccurate and unreliable model for the highly intermittent solar generations. However, the authors’ proposed uncertainty set utilises the forecast models that rely on the clear-sky and overcast solar forecasts, which are more accurate and reliable. The uncertainty set for solar generations is constructed based on the type of day, levels of daily uncertainty index (DUI) and daily energy index (DEI), and the uncertainty level of solar ramps. The test results on the IEEE 118-bus test system demonstrate that (i) using DEI and DUI reliably and efficiently manages and reduces the cost for highly uncertain and overcast day compared to the CRUC and (ii) the RUC cost is much sensitive to the selected level of DUI and DEI than the uncertainty level of the solar ramps.

This study presents a novel algorithm for reshaping the load demand profile of smart grid via modifying the price signal based on demand response principle. This modification in price is performed in a way that encourages customers to change their consumption pattern in order to achieve maximum saving. The proposed algorithm takes into consideration the customer willingness to participate in demand response, demand elasticity, price change limit, and comfort level of customers by determining a price signal, which maximises their saving when shifting their flexible load for certain limited hours and satisfying the daily energy consumption. The proposed method is formulated in a way, which avoids the problem of load synchronisation in which the maximum demand point is shifted to the point of lowest price. The proposed algorithm is applied to a typical benchmark of smart grid with residential, commercial, and industrial loads. The simulation results prove the success of the proposed algorithm for maximising customer saving and achieving the objective of energy management via a new consumption pattern.

Smart grid technology has enabled the bidirectional power exchange between utility and consumers, owning renewable power sources. The major challenge to this feature is the intermittent nature of renewable sources such as photovoltaic (PV), which makes it difficult to exchange a definite amount of power with the grid. This study aims to address this challenge, by proposing a multifunctional PV–battery system to provide intermittency smoothening along with reactive power and harmonics compensation in grid-connected mode and an uninterrupted power supply mode to loads during the grid outage. The PV–battery system comprises of a boost converter, bidirectional DC–DC converter and a grid-connected voltage-source converter (VSC). The boost converter operates the PV array at the maximum power point. The bidirectional converter provides the battery charging and maintains constant DC-link voltage. The VSC uses a multimode controller, which has a current controller for grid-connected mode, voltage controller for standalone mode, a voltage and rate of change of power-based islanding detection and resynchronisation as per the IEEE 1547–2018 revised standard, for mode transitions based on grid availability. Simulated and test results validate the effectiveness of the proposed control in achieving the set objectives.

The realisation of large-scale utilisation of renewable energy and two-way interactive electricity consumption are the main goals of active distribution networks (ADNs). This study focuses on the spirit ‘open, equal, cooperate, and share’ the initiative distribution network and premises that users’ habits of energy use will not change, establishes the optimal dispatch model of the multi-stakeholder game. In order to effectively increase the enthusiasm and fairness of the participation of new stakeholders in the ADN to participate in grid coordination and optimisation, promote the consumption of renewable energy, the optimal relevance equilibrium strategy is found by using the multi-stakeholder equilibrium game with improved correlated equilibrium learning . The results of the example show that the multi-stakeholder game coordination optimisation dispatch strategy based on this study can guide users to participate in demand response, improve the load curve and the consumption of renewable energy, so as to the peak load shifting.

Demand response (DR) has emerged as a very important part of the future smart grid to enhance the power system reliability and frequency stability. This study presents the benefits of DR control and battery energy storage (BES) on frequency regulation in wind farm integrated power system. In this study, a generalised load frequency control model of a hybrid hierarchical DR control mechanism is developed by incorporating the inherent system parameters such as frequency dead-band, aggregated generations/devices and communication time delay and so on. The mathematical formulation for frequency stability and sensitivity analysis are carried out with respect to the frequency change coefficient for switching OFF the DR devices and frequency regulation service shared by conventional supplementary control, DR and BES. The extensive simulation results are presented for system frequency response (SFR) to support the theoretical analysis. It is found that DR has a significant effect to stabilise SFR. The observations drawn from analysis can be helpful for system operators/regional transmission organisation to investigate or plan for deciding the frequency threshold, minimum turn OFF time for individual DR devices, frequency coefficient factor and percentage share of frequency regulation services to restore the frequency quickly.

This paper evaluates the self-scheduling problem for solar-based compressed air energy storage (CAES) plant with capability of compression waste thermal energy recovery via information gap decision theory (IGDT) approach. This feature gives the plant the ability to make income through participation in the thermal energy market. Moreover, the proposed plant uses natural gas as input fuel, which makes the system flexible to operate as a simple cycle gas generator when the stored air is drained. The utilisation of renewable energies in spite of many benefits has some challenges to self-scheduling of the solar-based three state CAES plant such as volatility and unpredictability. In addition, the uncertain solar farm generation is modelled by IGDT method. By the proposed IGDT model, the plant can pursue risk-taker and risk-averse strategies to face with different situations related to uncertain parameter. Finally, the numerical results obtained from case studies validate the appropriateness of the proposed approach.

This study presents an interval based robust chance constrained (IBRCC) optimisation model for allocating demand response program to effective buses of the power systems considering wind uncertainty and equipment failures. In the proposed formulation, an interval based robust approach is applied to evaluate the highest uncertainty spectrum of the wind power generation that the power system can tolerate. Accordingly, to cope with the uncertainty sources, chance-based constraints are implemented. In the proposed IBRCC optimisation framework, the level of the optimal solution robustness is probabilistically maximised subject to a set of operational constraints. Besides, to facilitate the massive integration of uncertain wind generation and to mitigate congestion in the transmission grid, an efficient allocation, and scheduling scheme of demand response programs is proposed. The proposed model is evaluated on the IEEE 24 bus system.

There are many technical challenges for integration of renewable energy sources (RESs) in the context of microgrids. Among these challenges, spinning reserve energy management should be accurately considered in the microgrid scheduling system for a better system operation. This study presents a methodology to model and analyse a novel scheme to integrate RESs, particularly photovoltaic (PV) systems, in diesel generation-based isolated microgrids. The proposed approach considers the uncertainties of PV power generation and demand, simultaneously, by solving a bi-level multi-objective optimisation problem using information gap decision theory (IGDT). The proposed energy management system is formulated considering spinning reserve constraints and the uncertainties associated with PV power generation and load, by solving a unit commitment problem. This method, a non-probabilistic approach, does not require the probability density function of uncertain parameters and provides a robust framework to better understand the potential savings due to the PV integration. In order to test and perform the analysis, realistic data from a 20 MW hybrid PV project is used as a case study. Furthermore, the proposed method is compared with probabilistic techniques, such as Monte Carlo simulations and scenario-based stochastic programming technique. The presented studies demonstrate applicability of the proposed model for real microgrids.

In smart cities, the end users should play an active role to provide the flexibility services in demand response programmes of smart distribution grids. Moreover, electric vehicles (EVs), their main task being providing green transportation, can play an important role in providing distributed energy in smart cities. In this study, a stochastic problem is presented to manage flexibility of end users and charging operation of EVs in the distribution grid. Thus, the end-users, aggregators, and EVs are defined as players in the proposed problem. Additionally, three strategies are presented to manage energy centralised, decentralised, or pseudo-decentralised in the proposed stochastic problem. In the decentralised and pseudo-decentralised approaches, flexibility of consumers is managed autonomously. On the other hand, EVs are utilised independently by their corresponding owners only in the decentralised approach. Also, a 33-bus distribution network is considered to assess the performance of the proposed strategies regarding the expected costs of players and the impact of flexibility on transacted energy through aggregators, and the real-time market. On the basis of simulation results, it is concluded that decentralised strategy to manage energy flexibility and charging operation of EVs by the end users is the win–win strategy for end users and aggregators.

This study presents a thermo-electric model to implement the energy management system (EMS) of an off-grid system. The EMS is formulated as a unit commitment problem where thermal and electrical components are dispatched simultaneously to minimise system operation costs. Particular attention is paid to the operation of heat pumps. The model contemplates a variable coefficient of performance (COP) of heat pumps. COP is different for the cold water and hot water circuit and depends on the output temperatures of the heat pump and the heat pump operation mode. Furthermore, the heat pumps’ ability to switch operation modes is also considered as well as the limitation of heat pumps to operate when hot water thermal energy storage is nearly full. The developed model has been successfully applied to an existing off-grid system at Norvento Enerxia's headquarters in Spain by simulating its operation during a winter week. The impact of modelling multi-mode operation of heat pumps with variable COP has been compared with single-mode operations with either fixed or variable COP. The proposed integrated approach of solving the electrical and thermal dispatch simultaneously has also been compared with a sequential resolution of first the thermal and then the electrical dispatch.

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment, and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimisation and decision analysis is put forward to address this model. First, a powerful heuristic optimisation algorithm, called the θ-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. Thereby, the best compromise solutions are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

In traditional integrated electricity and district heating systems, the inflexible operation of combined heat and power units leads to a large amount of wind power curtailments during winter. The thermal inertia of aggregated buildings can provide additional operational flexibility to promote wind power accommodation. In this study, a day-ahead scheduling model for integrated electricity and district heating system considering the thermal inertia of buildings is proposed. In this work, the operation model of the district heating network under constant mass flow and variable temperature operation strategy is presented, and the aggregated model of buildings based on the detailed thermal model of buildings is established. Then, the scheduling framework is analysed and the day-ahead scheduling model is formulated as quadratic programming problem to minimise the operation cost of integrated electricity and district heating system. The validity of the proposed model is verified by the case studies performed on a 6-bus power system with a 6-node heating system and IEEE 39-bus electricity system with a 16-node heating system. The results demonstrate that the thermal inertia of buildings can provide additional operational flexibility and effectively help reduce wind power curtailment and operation costs.

The role of demand side management (DSM) has changed the scenario of control and management in a microgrid. DSM works on real time without utilising any additional equipment in the network. The study proposes an all-day voltage control methodology using DSM. The contribution of this study is threefold. Firstly, a novel method for the day ahead voltage profile improvement is proposed by shifting flexible loads using demand and generation forecast information. Secondly, the proposed method uses only two parameters, the impedance of the network and amount of power injected to the flexible loads, in order to influence the voltage level and to improve overall voltage profile of the system. Thirdly, factor matrix is introduced; it decides which flexible loads to be shifted to regulate voltage within tolerance limits. In order to verify the effectiveness of the proposed voltage control method, it is tested on an low voltage (LV) distribution system considered as a microgrid. The result indicates that when the flexible loads are shifted with proper margin, the voltage of the system is contained within a limit and results in better load distribution throughout the day.

Multi-carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs’ facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters. The effect of the market prices, demands and solar radiation uncertainties on the SREH scheduling problem is characterised through a risk-constrained two-stage stochastic programming model. The objective of the proposed scheduling problem is to determine the least-cost 24 h operation of the facilities that would cover the cooling, thermal and electrical demands. The Monte Carlo simulation method is applied to model the inaccuracies of solar radiation, energy demands, and electricity market prices. Additionally, a proper scenario-reduction algorithm is employed to reduce the number of scenarios and simulation burden. The proposed approach evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling. Finally, an illustrative example is provided to confirm the efficiency and the applicability of the proposed approach.