Plug-and-Play Planning and Operation of N Grid-Connected Microgrids Under Uncertainty: A Data-Driven Optimization Framework Using Open French Load Profiles

Distribution system operators' (DSO) operating environment is changing significantly since customers' distributed production, electric vehicles, heat pump systems and battery energy storage systems (BESS) are becoming more common. In this paper, the proportion of rural area customers in Nordic conditions who could go off-grid and operate independently by investing in solar photovoltaics (PV) and BESS is studied. Further, the effects on these customers' electricity bill is studied, and thereby to grid defection profitability, if DSO changes tariff system towards power-based tariff (PBT) is examined. Analyses are conducted with real customers' hourly load data from four different case areas, covering a total of 10808 customers. The study shows that the number of possible customers in the studied rural case areas is 8-19 %. If DSO changes the tariff structure from energy-based tariffs to power-based tariffs, it has significant effects on grid defection profitability whether the tariff structure includes minimum charged power or not.

In the past few years, high penetration of Renewable Energy Sources (RES) in transmission and distribution networks has challenged the stability of the power grid due to the intermittent nature of the sources, mainly wind and solar energy. A Battery Energy Storage System (BESS) can be considered as a fast-acting backup source to accommodate higher penetration of RES. A BESS has better ramping characteristics than traditional generators to smooth out the RES fluctuations. The limitation of power ramping capability of PV plants in meeting ANSI C84.1 standard in distribution networks is addressed in this paper. The BESS is added in parallel with the PV plant with the aim of minimizing voltage variations of the grid at the Point of Common Coupling (PCC). Due to grid characteristics, different power ramping capability of the BESS is required to mitigate PV plant intermittency. An analytical method is introduced to find the required BESS power and energy ratings considering network characteristics, geographical variables like wind speed, and size of the PV plant. The IEEE 13-bus system is selected as a test case for investigating the voltage sensitivity to the injected active power. Finally, the minimum required ratings of a BESS is calculated and validated with a common BESS sizing method using historical data.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

Renewable energy resources (RESs) are significantly integrated in distribution networks to promote green technologies in future power systems. The idea of microgrids (MGs) is developed for the efficient use of RESs through an appropriate control, monitoring and management system. Control and management of MGs are challenging tasks along with numerous economic and environmental benefits. The challenges of MGs operation include tie-line power fluctuations that have an adverse effect on the stability and quality of distribution networks. Tie-line power control in a residential MG is difficult due to dependency on RESs as a primary generation unit in MGs. Motivated by these, this thesis investigates the tie-line power control issues in grid-connected residential MGs and applies several controls and optimisation methods to achieve a smooth tie-line power satisfying system boundary conditions. First, a dynamic energy management system (EMS) is designed to reduce the tie-line fluctuation in a grid-connected MG through an indirect grid power control strategy. A fuzzy logic-based EMS is proposed to control the battery power due to the variations in generations and loads. The net power demand and battery state of charge (SoC) of an MG are considered inputs of the fuzzy controller to determine the battery power by keeping the battery SoC within limits. An offline optimisation method is used to optimise the membership functions and rules to shape the performance parameters. Thereafter, a golden section search-based non-linear programming method is applied to design a battery power management system to minimise the tie-line fluctuation in an MG counting the system constraints and disturbances. Two other rule-based methods are also demonstrated for comparative analysis of the proposed methods in terms of predefined performance parameters. Afterward, a dynamic grid power control method is presented to control the interlink inverters in grid-connected MGs. A grid power controller is designed based on a complete model of the MG systems to achieve a constant tie-line power on typical days of the year. The designed controller can effectively smooth tie-line fluctuation in a grid-connected residential MG. The charging/ discharging of the battery is controlled by a DC-DC converter which is also responsible to provide a stable DC bus to the input of an interlink inverter. The reference tie-line power is determined by a MG controller based on statistical power generations, load demand and battery SoC. Moreover, an eigenvalue-based stability analysis is performed to show the sensitivity of system parameters on system stability. Furthermore, the tie-line power control in a networked MG (NMG) is investigated to obtain a smooth tie-line power in an NMG connected to a common bus. A model predictive control-based distributed power flow controller is proposed to control the interlink inverters of the NMG in a distributed manner. Charging/ discharging of battery is controlled by a decentralised model predictive power controller to provide a stable DC voltage for MGs. Communication between MGs is performed for sharing the status of the tie-line power along with the scheduled tie-line reference. The information from the network is used to determine the instantaneous reference grid power of individual MGs for achieving a smooth tie-line power for the network. Inverter switching actions are performed to minimise the difference between predictions and references. In addition, a comparative study with a decentralised operation of MGs is conducted to show the benefits of networked operation. All the proposed methods are tested through rigorous case studies to validate the performance despite the variations in input and output system disturbances. Comparative analysis among different methods is also conducted to demonstrate the performance variations through adopting different methods. For the simulation experiment set up, MATLAB SIMULINK Simscape Electrical is used to develop a designed system model of MGs and experimental models of the proposed methods. Experiments are performed using real weather and residential load information in Queensland, Australia. The results demonstrate that the proposed methods have achieved the design objectives to solve the tie-line fluctuation problem of grid-connected residential MGs.

In many applications combined heat and power (CHP) systems provide both cost and environmental benefits when used in place of traditional grid utility systems. The benefits of CHP systems, however, are very sensitive to the proper selection of the system’s components for given loading conditions, especially the power generation unit (PGU). The PGU size should be selected based on the facility’s electrical and thermal loading conditions to generate the maximum cost savings. Many researchers have presented methods for selecting the optimal PGU size using hourly load data. However, hourly load data is seldom available for a facility. For this reason, this paper provides a series of calculations that determine the optimal PGU size based on annual cost savings for a base-loaded CHP system using monthly load data, which is representative of a facility’s utility bills that are always available. Furthermore, because resale of excess electricity to the grid is widely available in the United States and other countries, this capability is included in the equations presented in this paper. Unfortunately, in some cases monthly-based calculations do not provide enough information to properly determine the optimal size of the PGU. A case study is performed to compare the results of monthly-based and hourly-based calculations for sixteen benchmark buildings in Albuquerque, NM and Baltimore, MD to investigate when monthly-based calculations can be used. It is noted that resale of excess electricity is considered in Baltimore, but not considered in Albuquerque for this study. A statistical analysis of the results uncovers that the magnitude of the coefficient of variation of the building’s heating demand indicates whether monthly-based calculations can be used, and furthermore, whether annual cost savings are possible. Results indicate that the proposed monthly-based calculations can determine the optimal PGU size, and calculate annual cost savings for approximately half of the facilities in Albuquerque and Baltimore. Further studies must be performed to determine a broader range of locations where this monthly-based calculation strategy is valid.

This paper proposes a new method to determine the optimal size of a photovoltaic (PV) and battery energy storage system (BESS) in a grid-connected microgrid (MG). Energy cost minimization is selected as an objective function. Optimum BESS and PV size are determined via a novel energy management method and particle swarm optimization (PSO) algorithm to obtain minimum total cost. The MG was designed to use its own energy as much as possible, which is produced from renewable energy resources. Since it is a grid-connected system, it can demand energy from the grid within the determined limit with penalty. It differs from the studies in the literature in terms of optimizing both parameters such as PV and BESS size, being a grid-connected self-contained MG structure and controlling the grid energy by an energy management algorithm and optimizing the parameter via PSO with an energy management system (EMS). Results are compared for different PV and BESS. Moreover, effectiveness of the novel energy management method with PSO is compared with the genetic algorithm, which is the one of the well-known optimization algorithms. The results show that the proposed algorithm can achieve optimum PV and BESS size with minimum cost by using the new energy management method with the PSO algorithm.

Two-dimensional colour pattern load analysis: A tool supporting demand-side management

Deterministic power management strategy for fast charging station with integrated energy storage system

Forecasting of electrical load is extremely important for the effective and efficient operation of any power system. Good forecasts results help in minimizing the risk in decision making and reduces the costs of operating the power plant. This work focuses on the short-term load forecast of the 132/33KV transmission sub-station at Port-Harcourt, Nigeria, using the Artificial Neural Network (ANN). It provides accurate week-ahead load forecast using hourly load data of previous weeks. ANN has three sections namely; input, processing and output sections. There are four input parameters for the input section which are historical hourly load data (in MW), time of the day (in hours), days of the week and weekend while the output parameter after the processing (i.e. training, validation and test) is the next week hourly load predicted for the entire system. The technique used is the artificial neural network with the aid of MATLAB software. It was proven to be a good forecast method as it resulted in R-value of 0.988 which gives a mean absolute deviation (MAD) of 0.104 and mean squared error (MSE) of 0.27. Keywords : Load forecast, transmission substation, artificial neural network, power system JEL Classifications: C63, L94, L98, Q48 DOI: https://doi.org/10.32479/ijeep.8629

Battery energy storage system (BESS) is a crucial technology for managing various uncertainties and key challenges particularly, peak shaving, inherent in regional distribution networks (RDNs). However, an improperly sized BESS can lead to unreasonable installation, operation, and maintenance costs. Considering that these costs may exceed the operational benefits of the battery, this work establishes an analytical approach for the optimal sizing of BESS aimed at cost-effective peak-shaving applications, especially in an Australian RDN. The procedure utilizes the RDN load profiles, characterized by a determined time resolution, while accounting for various billing rates and electricity costs. Utilizing real load and cost data, this approach systematically determines the optimal battery capacity from various BESS configurations, enhancing the overall efficiency and performance of the BESS. The proposed analytical method is evaluated using a rule-based technique, ensuring practical applicability and reliability. The results, tested on a real Australian RDN, demonstrate that the approach can significantly determine the most economically suitable BESS configuration, reduce system operational costs, and achieve effective peak shaving during high-demand periods. Additionally, to evaluate the feasibility of the technique, load profiles and associated cost factors also have been collected from a Malaysian RDN, tested in the case study. • An analytical approach is developed for optimal sizing of BESS for peak shaving in regional distribution networks. • The proposed method is tested using real load profiles and electricity cost data from Australian and Malaysian RDNs. • A rule-based evaluation confirms the practical applicability and economic benefits of the optimal size of BESS. • Results demonstrate that the approach effectively reduces operational costs and enhances system efficiency.

Distribution networks (DN) have transformed more than ever before due to the penetration level of distributed energy resources. One of the promising technologies to generate power at the consumer level is the photovoltaic system (PV). To facilitate a higher level of PV penetration, DN planning and system operators encounter several challenges related to power reliability, stability, and quality that are affected by the intermittent nature of PV. A practical solution to overcome the uncertainty behavior of PV s is to use a Battery Energy Storage System (BESS). However, the appropriate placement of the BESS in the DN plays a significant role to mitigate the higher level of power losses. Hence, optimal allocation is extremely desirable to maximize the benefits of BESS. In this paper, the optimal allocation of BESS in a DN with a high penetration level of the PV system is examined towards power losses reduction. The optimal allocation of BESS in DN performed using a genetic algorithm optimization technique. The optimal placement of BESS in DN was compared between an aggregated BESS and distributed BESS. The outcomes of both of them showed a reduction in power losses, where the optimal allocation in distributed BESS has the highest power losses reduction.

The integration of distributed energy resources has modernized the Distribution Networks (DNs) significantly. The recent cost reduction and technical advancement of photovoltaic (PV) applications encourage generating electricity at the distribution level more economically. However, the unpredictable behavior of PV creates several difficulties related to power system stability, quality, and reliability. The high penetration level of PV systems increases the challenge for DN system operators and planners. A practical solution to overcome the uncertainty behavior of PVs is to use a Battery Energy Storage System (BESS). However, the appropriate placement of the BESS in the DN plays a significant role to mitigate the higher level of power losses. Hence, optimal allocation is extremely desirable to maximize the benefits of BESS. In this paper, the optimal allocation of BESS in a DN with a high penetration level of the PV system is examined towards power losses reduction. The IEEE 33-bus system is utilized in this study as a testing model. A genetic algorithm optimization technique is used to find the optimal location of BESS in DN. The optimal placement of a bulk BESS and distributed BESS in DN were compared. The results demonstrated a considerable reduction in power losses for the bulk BESS and further reduction in power losses is achieved in the case of distributed BESS.

Distributed generation (DG) sources are becoming more popular nowadays and they are inevitable in modern large interconnected power systems to meet enormous demands of electrical energy. Such sources could be photovoltaic cells, wind generation, biomass, combined heat power, fuel cells etc. The location and size of distributed generations (DGs) have more impact on energy losses, voltage stability and other benefits also. A method has been proposed in this paper for the placement of DGs in a distribution network. The best nodes once identified, power injection by DGs is optimised using genetic algorithm and particle swarm optimisation methods to reduce power losses, maintain node voltages and branch currents within specified limits. The methods are also compared for getting the best penetration level of DGs at different load levels. It is observed that reconfiguration with DGs at their locations have great impact on power loss reduction and voltage stability improvement. The effect of power injection by DGs and reconfiguration with DGs to the voltage stability of distribution network is examined using a voltage stability index. The proposed technique is demonstrated through an example of 69 node distribution network.

Most design programs for heat exchangers design for Groundsource heat pumps use a superposition of annual, monthly and hourly pulses. Some design methods use monthly and hourly loads. Hourly load simulations are used mostly for simulation purposes but not for design due to longer simulation time. The authors propose a new design approach based on hourly load data with the aid of an accelerated algorithm. The new approach does not use aggregation of loads and simulate the real thermal response of the individual hourly loads.

The paper discusses a method to get the hourly load data from the peak daily load (i.e. to store one value and get 24 values). The method uses one of the intelligent techniques (artificial neural network ANN ). The annual load duration (ALDC) is used in different studies, such as power system planning, reliability study etc. In this work the ALDC is used as an example for the application of the proposed method. When the hourly load data for a year are available, it is easy to find the ALDC. In the studies where the ALDC is needed, the load is usually forecasted (future load ) where the hourly data are not available. A proposed ANN is explained to overcome this difficulty. The method develops the daily load (24 hours) from the peak load. The required days are encountered, which means that the ALDC is obtained for the required days. Also if there is a missing period, the proposed method can develop that missing period in the data. The data of the Iraqi North Region National Grid (INRNG) for the year 2001 is used to verify the validity of the proposed method. The results of a conventional method are also given.