An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm

Optimal operation method coping with uncertainty in multi-area small power systems

Japan contains a vast number of isolated islands. Majority of these islands are poweredby diesel generators (DGs), which are operationally not economical. Therefore, the introduction of renewableenergy systems (RESs) into these area is very much vital. However, the variability of RESs asa result of weather condition as well as load demand , battery energy storage system (BESS) is broughtinto play. Demand response (DR) programs have also been so attractive in the energy management systemsfor the past decades. Among them, the real-time pricing (RTP) has been one of the most effectivedemand response program being utilized. This program encourages the customer to increase or reducethe load consumption by varying the electricity price. Also, due to the increase in power transactionmarket, Japan electric power exchange (JEPX) has established spot (day-ahead), intraday hour-ahead,and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these marketscan make it possible to deal with uncertainty related to generated power fluctuations. Therefore,this paper presents the optimal operation method coping with the uncertainties of RESs in multi-areasmall power systems. The proposed method enables flexibility to correspond to the forecasting error byproviding two kinds of power markets among multi-area small power systems and trading the shortageand surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, theproposed method was able to reduce operational cost for multi-area small power systems. The processof creating operational plan for RTP, power trading at the markets and the unit commitment of DGs arealso presented in this paper. Simulation results corroborate the merit of the proposed program