Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction

Heuristic Optimization for Microload Shedding in Generation Constrained Power Systems

While the causes of power system outages are often complex and multi-faceted, an apparent deficit in generation compared to a known demand for electricity could be more alarming. A sudden hike in demand at any given time may ultimately result in the total failure of an electricity network. In this paper, algorithms to efficiently allocate the available generation is investigated. Dynamic programming based algorithms are developed to achieve this constraint by uniquely controlling home appliances to reduce the overall demands for electricity by the consumers on the grid in context. To achieve this, heuristic optimization method (HOM) based on the consumers’ comfort and the benefits to the electricity utility is proposed. This is then validated by simulating microload management in generation constrained power systems. Three techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS) techniques were studied for effecting efficient microload shedding. The research is aimed at reducing the burden imposed on the consumers in a generation constrained power system by the traditional load shedding approach. Additionally, the reduction of the excess curtailment is a prime objective in this paper as it helps the utility companies to reduce wastage and ultimately reduce losses resulting from over shedding. Reducing the peak-to-average ratios (PAR) on the entire network in context as a critical factor in the determination of the efficiency of an electricity network is also investigated. In the long run, the PAR affects the price charged to the final consumer. Simulation results show the associated benefits that include effectiveness, deployability, and scalability of the proposed HOM to reduce these burdens

Electricity Demand Forecasting for Microload Management

This data set comprises microload consumption extracted and processed from one home in Vancouver, Canada, from April 2012 to the end of March 2014 [1]. The different microloads’ data were cleaned and processed. The weather data was hourly, and the microload consumptions were in minute intervals. The time interval of one hour was processed into minutely weather data. Three months of data comprising the weather and the microloads was selected from the process data and used to generate what is presented here. Reference: [1] S. Makonin, “Ampds2: the almanac of minutely power dataset (version2),” Harvard Dataverse, p. V2, 201

Improving Electricity Network Efficiency and Customer Satisfaction in Generation Constrained Power System

Electricity situation in generation constrained power systems creates a high level of inconvenience for both utilities and their consumers. In this paper, we examined the causes of the constraints and the mitigating methods being adopted. The demand for electricity cannot be allowed to exceed the available generation as it could cause the entire power system to collapse. Therefore, electricity utilities are motivated to turn off some sections of the network in order to reduce the demand. Lack of funds makes it difficult for such systems to increase their generation capacity. A key concern is the hardship imposed by the sectional blackouts that they create. Smart metering promises an interim solution but the cost of deployment is of great concern. We proposed a smart metering simulation tool which is then used to model a micro-load manageable smart metering system based on certain priorities of loads. Algorithms and optimisation techniques are then developed to micro-load manage the demand so as to maintain some level of customer essential energy requirements. Simulation result shows that the proposed system is efficient in micro-load managing electricity demand. The proposed system has the potential to prevent total blackouts and associated inconveniences as well as improve the efficiency of such power systems

Optimal Demand Side Management in Generation Constrained Power Systems

In the face of dwindling generation from hydroelectric generators in most developing countries coupled with a continuous increase in electricity demand is forcing the electric utility companies to effect various demand-side management (DSM). Amongst the DSMs being implemented in these countries is the severe load shedding method that seeks to reduce the overall demand by cutting off sections of a grid resulting in a complete power cut to the affected areas. In this paper, the potential of the smart grid is harnessed to enable microload shedding to avoid a complete cut-off from the grid. Algorithm for efficient allocation of available generation is proposed. Dynamic programming-based algorithms are developed to achieve this constraint by granularly controlling home appliances to reduce the overall demands for electricity by the consumers on the grid. The efficacy of the proposed system is proven by the simulation results obtained

An algorithm for micro-load shedding in generation constrained electricity transmission network

Adequate generation capacity to meet the increasing demand of electricity has remained the main focus of both developed and developing economies. In order to maintain the safety generation and distribution systems, constant load shedding is employed to maintain equilibrium between overall generation and total demand per unit period. This paper proposes a distribution management algorithm to micro-load manage the load shedding process by considering households and all electricity based devices as unique loads that can be individually managed. Consequently, the proposed algorithm prevents total blackout in communities affected by the load shedding. Experiments were conducted on a simulation environment made up of over a 25 households each equipped with a smart meter, and loads of varied quantity and types. The results obtained from the experiment show that our proposed micro-load management algorithm is effective for micro-load managing demands in near real time