An Optimal Dispatch Framework of Electric and Heating Networks Based on Controllable Electric and Thermostatically Controlled Loads

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.

Nanogrid topology, control and interactions in a microgrid structure

<p>Distributed generation, in the form of small-scale photovoltaic installations, have the potential to reduce carbon emissions created by, and alleviate issues associated with, centralised power generation. However, the major obstacle preventing the widespread integration of small-scale photovoltaic installations, at a residential level, is intermittency. This thesis addresses intermittency at a household/small community level, through the use of "nanogrids". To date, ambiguity has surrounded the nanogrid as a power structure, which is resolved in this thesis through the derivation of concise nanogrid definition. The nanogrid, a power distribution system for a single house/small building, is then used to implement demand side management within a household. This is achieved through the use of a hybrid central control topology, with a centralised coordinating controller and decentralised control nodes that have the ability to sense and modulate power flow. The maximum power point tracker is used to observe the available photovoltaic power, and thermostatically controlled loads present in the household are manipulated to increase the correlation between power production and consumption. An algorithm is presented which considers the expected power consumption of the thermostatically controlled loads over a 24 hour period, to create a hierarchical ratio. This ratio determines the percentage of available photovoltaic power each load receives, ensuring the loads that are expected to consume the most power are serviced with the largest ratio of photovoltaic power. The control system is simulated with a variety of household consumption curves (altered for summer/winter conditions), and a week of realistic solar irradiance data for both summer and winter. In each simulated scenario, a comparison was made between controlled and uncontrolled households to ascertain the extent grid power consumed by a household could be reduced, in turn reducing the effect of intermittency. It was found that the system had the ability to reduce the grid power consumed by as much as 61.86%, with an average reduction of 44.28%. This thesis then explores the concept of interconnecting a small community of nanogrids to form a microgrid. While each nanogrid within the network has the ability to operate independently, a network control strategy is created to observe the possibility of further reducing grid power consumed by the community. The strategy considers the photovoltaic power production and thermostatically controlled loads operating within the network. A ratio of the network's photovoltaic power is distributed to the thermostatically controlled loads, based on their expected consumption over a 24 hour period (highest consumption receives largest ratio of power). This was simulated with a range of household cluster sizes, with varied consumption patterns, for a week with summer/winter solar irradiance. The tests show that, compared to an uncontrolled nanogrid network, the combined control can reduce grid power consumed by as much as 55%, while a 7% decrease is seen when comparing the combined control to the individually controlled nanogrid networks. When compared to an uncontrolled individual house scenario, the combined control interconnected nanogrids can reduce the power purchase from the grid by as much as 61%.</p>

Nanogrid topology, control and interactions in a microgrid structure

<p>Distributed generation, in the form of small-scale photovoltaic installations, have the potential to reduce carbon emissions created by, and alleviate issues associated with, centralised power generation. However, the major obstacle preventing the widespread integration of small-scale photovoltaic installations, at a residential level, is intermittency. This thesis addresses intermittency at a household/small community level, through the use of "nanogrids". To date, ambiguity has surrounded the nanogrid as a power structure, which is resolved in this thesis through the derivation of concise nanogrid definition. The nanogrid, a power distribution system for a single house/small building, is then used to implement demand side management within a household. This is achieved through the use of a hybrid central control topology, with a centralised coordinating controller and decentralised control nodes that have the ability to sense and modulate power flow. The maximum power point tracker is used to observe the available photovoltaic power, and thermostatically controlled loads present in the household are manipulated to increase the correlation between power production and consumption. An algorithm is presented which considers the expected power consumption of the thermostatically controlled loads over a 24 hour period, to create a hierarchical ratio. This ratio determines the percentage of available photovoltaic power each load receives, ensuring the loads that are expected to consume the most power are serviced with the largest ratio of photovoltaic power. The control system is simulated with a variety of household consumption curves (altered for summer/winter conditions), and a week of realistic solar irradiance data for both summer and winter. In each simulated scenario, a comparison was made between controlled and uncontrolled households to ascertain the extent grid power consumed by a household could be reduced, in turn reducing the effect of intermittency. It was found that the system had the ability to reduce the grid power consumed by as much as 61.86%, with an average reduction of 44.28%. This thesis then explores the concept of interconnecting a small community of nanogrids to form a microgrid. While each nanogrid within the network has the ability to operate independently, a network control strategy is created to observe the possibility of further reducing grid power consumed by the community. The strategy considers the photovoltaic power production and thermostatically controlled loads operating within the network. A ratio of the network's photovoltaic power is distributed to the thermostatically controlled loads, based on their expected consumption over a 24 hour period (highest consumption receives largest ratio of power). This was simulated with a range of household cluster sizes, with varied consumption patterns, for a week with summer/winter solar irradiance. The tests show that, compared to an uncontrolled nanogrid network, the combined control can reduce grid power consumed by as much as 55%, while a 7% decrease is seen when comparing the combined control to the individually controlled nanogrid networks. When compared to an uncontrolled individual house scenario, the combined control interconnected nanogrids can reduce the power purchase from the grid by as much as 61%.</p>

Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.