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This article presents internal model control (IMC) based decentralized reinforce‐control of renewable dynamic virtual power plant (DVPP) so that, it can be integrated into the power system as a substitution of fuel‐based conventional generators. Such grid integration towards net‐zero targets could not be possible without providing additional ancillary service (AS) to the power system, as the traditional AS would fall short with the retirement/substitution of conventional generators. The theory of DVPP from a technical perspective (i.e., TDVPP) is presented in a detailed and simplified manner, including the formulation of a generalized control objective (desired specification) for DVPP integration. The solution approach includes two steps: (1) disaggregation of desired specification and (2) decentralized reinforce‐control to match the disaggregated specification. The theory and solution approach for DVPP integration is presented in a generalized manner enabling the DVPP to offer multiple ASs, but the case study is limited only to frequency control AS (FCAS) in this article. The study is performed on the 'western system coordinating council (WSCC)' test system, in which an attempt is made towards net‐zero targets by substituting the largest thermal generator with renewable DVPP ensuring the grid's operation or dynamics safe. [ABSTRACT FROM AUTHOR]

The universal power demand is increasing every day because of population growth, industrial revolution and high standards of living. The renewable energy technologies (RETs) are environmental and cost‐effective solutions to satisfy dynamic load profile based on the application of multiple components of a standalone microgrid system that encompasses photovoltaic (PV), diesel generator (DG), electric storage system and wind turbine generator (WTG). This study reveals more insight into various research questions that have not been completely addressed in past investigations. The contributions of the study are expressed: (i) analyse the effects of RETs on the total cost, (ii) test the total benefits, (iii) estimate the market benefit share of the unit that reflects the benefit of one generation unit in proportion to the whole power system, (iv) maximise and minimise the yearly average power generated by (PV and WTG) and DG. This research work presents the application of fmincon optimising tool to accomplish the objectives of the study owing to some significant characteristics. The outcomes of the work show the validity of the model and can assist the utilities to minimise the costs that are identified with the optimal operation of their power systems. [ABSTRACT FROM AUTHOR]

The cost of producing electricity with solar photovoltaic (PV) has decreased drastically in the past 10 years, so much that the installed PV capacity has increased exponentially between 2010 and 2018. South Africa is endowed with a technical potential of 72 GW for PV rooftop systems, but the economic potential is largely unknown. As a result, the integrated resource plan assumes that an annual installation of 200 MW of distributed generation will be made between 2018 and 2030. This study evaluates the economic potential of rooftop PV systems in the residential sector within metropolitan cities in South Africa using financial metrics. The important financial metrics used to qualify an investment are net present value, internal rate of return (IRR) and payback period. An investment is expected to pay itself back in less than 10 years with a high rate of return (IRR). Households are classified into living standards measure so that it is easier to map customers to related tariffs. Using this method, the eight metropolitan municipalities will have PV economic potential of 11 GW in 2019 and 22.7 GW by 2030 if the current tariff grows at 5% and the tariff structure does not change. [ABSTRACT FROM AUTHOR]

This study explores optimisation of the hybrid power system in the smart grid framework, in conjunction with the model predictive control (MPC) design. This study also creates a strategy that can maximise the use of renewable energy, e.g. photovoltaic, the wind turbine with battery storage and minimise the utilisation of the utility grid for electricity usage in the industry. This is devised by modelling a discrete state-space model of the hybrid power system for a given industry application. The system design is implemented within a real-time electricity pricing environment that is integrated with renewable energy to optimally meet the demand according to a specific performance of the consumer. The emphasis of this approach is on its capacity to supply optimal power to the demand side by selecting the appropriate source; and its robustness against uncertainties. The results show that MPC design for hybrid power system not only optimises the energy flow but also improves the overall process of energy management. It was also observed that the optimal solution minimises the delay cost of energy demand from the utility grid according to a given reference from the consumer for the specified tuning parameter values of the performance index. [ABSTRACT FROM AUTHOR]

This study explores optimisation of the hybrid power system in the smart grid framework, in conjunction with the model predictive control (MPC) design. This study also creates a strategy that can maximise the use of renewable energy, e.g. photovoltaic, the wind turbine with battery storage and minimise the utilisation of the utility grid for electricity usage in the industry. This is devised by modelling a discrete state‐space model of the hybrid power system for a given industry application. The system design is implemented within a real‐time electricity pricing environment that is integrated with renewable energy to optimally meet the demand according to a specific performance of the consumer. The emphasis of this approach is on its capacity to supply optimal power to the demand side by selecting the appropriate source; and its robustness against uncertainties. The results show that MPC design for hybrid power system not only optimises the energy flow but also improves the overall process of energy management. It was also observed that the optimal solution minimises the delay cost of energy demand from the utility grid according to a given reference from the consumer for the specified tuning parameter values of the performance index. [ABSTRACT FROM AUTHOR]