Describes software downloadable from: https://github.com/peterboait/ESCoBox_Load_Model\ud \ud Open AccessThe potential for small self-contained grid systems to provide electricity for currently unserved regions of the developing world is widely recognised. However planning and managing the electrical demand that will be supported, so that a mini-grid system is not overloaded and its available resource is used as fully as possible, is actually more difficult than for a large scale grid system. This paper discusses the mathematical reasons why this is the case, and describes a practical software tool for mini-grid demand estimation and planning that is complementary to the widely used HOMER software. This software tool is made available for download on an open source basis. Finally a conclusion is offered that mini-grid systems should aim to serve at least 50 households so that demand variability is more manageable and economies of scale can be realised

Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual rate of about 7% as a result of population and economic growth. The consumption of the residential sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of air-conditioning (AC) systems has become 70% of residential buildings’ total electricity consumption in the summer months, leading to a high peak electricity demand. This study investigates solutions that will tackle the problem of high energy demand associated with KSA’s air-conditioning needs in residential buildings. To reduce the AC energy consumption in the residential sector, we propose the use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior based on behavioral data obtained through a survey. The potential impacts in terms of achievable electricity savings of different AC operation modes for residential houses of Riyadh city are presented. The results from our computer simulations show that the solutions intended to reduce energy consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40% increase in total annual energy savings.

The ability to influence electricity demand from domestic and small business consumers, so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signalling to consumers to indicate when electricity use is desirable or undesirable. However, simply signalling a time‐dependent price does not always achieve the required demand response and can result in unstable system behaviour. The authors propose a demand response scheme, in which an aggregator mediates between the consumer and the market and provides a signal to a ‘smart home’ control unit that manages the consumer's appliances, using a novel method for reconciliation of the consumer's needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, the authors show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement.