Smart Grid and Microgrid - Revision history

Yunsi: /* Publications */

2014-08-06T15:50:22Z

Publications

← Older revision		Revision as of 15:50, 6 August 2014
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	=Publications=		=Publications=
-	*B. Jiang, Y. Fei, “Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents”, in Proc. IEEE Int. Conf. on Smart Grid and Clean Energy Technologies, Sep. 2011.	+	*{{#lst:Publications\|bjj1}}
		+	*B. Jiang and Y. Fei, “Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents”, in Proc. IEEE Int. Conf. on Smart Grid and Clean Energy Technologies, Sep. 2011.

Bnjiang at 16:21, 21 August 2011

2011-08-21T16:21:17Z

← Older revision		Revision as of 16:21, 21 August 2011
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	=Description=		=Description=
	==Background==		==Background==
-	Sustainability has become an imperative requirement on many infrastructures and systems of our society with the impending energy crisis and environment deterioration.Power grids are being transformed into [http://en.wikipedia.org/wiki/Smart_grid Smart Grid] with advanced sensors, information and communication technologies.In smart grid, the energy flow will become two-way between the grid and consumers with renewable energy generations, which will be monitored and controlled by sensors, smart meters, digital controls and analytical tools. Smart Grid usually introduces [http://en.wikipedia.org/wiki/Demand_response Demand Response (DR)] and [http://en.wikipedia.org/wiki/Distributed_generation Distributed Generation (DG)] to improve energy consumption and generation efficiency.	+	Sustainability has become an imperative requirement on many infrastructures and systems of our society with the impending energy crisis and environment deterioration. Power grids are being transformed into [http://en.wikipedia.org/wiki/Smart_grid Smart Grid] with advanced sensors, information and communication technologies. In smart grid, the energy flow will become two-way between the grid and consumers with renewable energy generations, which will be monitored and controlled by sensors, smart meters, digital controls and analytical tools. Smart Grid usually introduces [http://en.wikipedia.org/wiki/Demand_response Demand Response (DR)] and [http://en.wikipedia.org/wiki/Distributed_generation Distributed Generation (DG)] to improve energy consumption and generation efficiency.

	==Purpose==		==Purpose==
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	===Shared Cost-led CHP Management===		===Shared Cost-led CHP Management===
-	In this section, the generation of micro CHP systems and power consumption of electric heat pumps are optimized to minimized the energy consumption cost of the whole community. The constraints include the maximum electric power of each house, thermal power balance, micro CHP fuel consumption range and heaters' power range. The ~~optimizatio~~ problem at nth decision period can be formed as:	+	In this section, the generation of micro CHP systems and power consumption of electric heat pumps are optimized to minimized the energy consumption cost of the whole community. The constraints include the maximum electric power of each house, thermal power balance, micro CHP fuel consumption range and heaters' power range. The optimization problem at nth decision period can be formed as:
	<math>\min \qquad \sum\limits_{k = 0}^{N - 1} {{C_{CME}}(k + nN) + {C_{CMF}}(k + nN) - {B_{CM}}(k + nN)}		<math>\min \qquad \sum\limits_{k = 0}^{N - 1} {{C_{CME}}(k + nN) + {C_{CMF}}(k + nN) - {B_{CM}}(k + nN)}
	</math>		</math>

Bnjiang: /* Publications */

2011-08-21T16:18:59Z

Publications

← Older revision		Revision as of 16:18, 21 August 2011
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	=Publications=		=Publications=
		+	*B. Jiang, Y. Fei, “Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents”, in Proc. IEEE Int. Conf. on Smart Grid and Clean Energy Technologies, Sep. 2011.

Bnjiang: /* People */

2011-08-21T16:17:06Z

People

← Older revision		Revision as of 16:17, 21 August 2011
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	=People=		=People=
		+	*[[Bingnan Jiang]]
		+
	=Publications=		=Publications=

Bnjiang: /* Dynamic Residential DR */

2011-08-21T16:15:57Z

Dynamic Residential DR

← Older revision		Revision as of 16:15, 21 August 2011
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	[[File:PRFunction.png\|thumb\|right\|300px\|Reward/Penalty function]]		[[File:PRFunction.png\|thumb\|right\|300px\|Reward/Penalty function]]
	Electricity demands are represented as schedulable and fixed energy consumption tasks. Dynamic DR focuses on the schedulable load. Schedulable tasks have their own convenience rate distributions, which reflect the satisfaction users can get when tasks are executed at different time.		Electricity demands are represented as schedulable and fixed energy consumption tasks. Dynamic DR focuses on the schedulable load. Schedulable tasks have their own convenience rate distributions, which reflect the satisfaction users can get when tasks are executed at different time.
-		+	<br\>
		+	<br\>
	The model of dynamic DR consists of dynamic task array, external information and task convenience rate function as inputs, and generate task scheduling as outputs. After scheduling, only tasks scheduled in current decision period are executed. Others will be rescheduled in the next decision period with updated external information.		The model of dynamic DR consists of dynamic task array, external information and task convenience rate function as inputs, and generate task scheduling as outputs. After scheduling, only tasks scheduled in current decision period are executed. Others will be rescheduled in the next decision period with updated external information.
-		+	<br\>
		+	<br\>
	The convenience rate function comprises Basic function and Reward/Penalty function. The basic function is used for initialization. The Reward/Penalty function serves as agents’ reaction towards user’s interference and is used to update the convenience rate function dynamically. The user may attempt to change task execution time scheduled by the agent. The new execution time is considered as the new comfort time for the user and will be given a maximum convenience rate increase. The old scheduled time will be penalized by decreasing its convenience rate with the same value.		The convenience rate function comprises Basic function and Reward/Penalty function. The basic function is used for initialization. The Reward/Penalty function serves as agents’ reaction towards user’s interference and is used to update the convenience rate function dynamically. The user may attempt to change task execution time scheduled by the agent. The new execution time is considered as the new comfort time for the user and will be given a maximum convenience rate increase. The old scheduled time will be penalized by decreasing its convenience rate with the same value.
-		+	<br\>
		+	<br\>
	DR agent will optimize the following cost function considering both energy consumption cost and user's convenience:		DR agent will optimize the following cost function considering both energy consumption cost and user's convenience:
	<math>\min \qquad J = \frac{{C_{{k_n}}^{}}}{{U_{{k_n}}^{}}} + (I - \sum\limits_{i = 1}^I {s_i^{{k_n}}} ){P_T}		<math>\min \qquad J = \frac{{C_{{k_n}}^{}}}{{U_{{k_n}}^{}}} + (I - \sum\limits_{i = 1}^I {s_i^{{k_n}}} ){P_T}

Bnjiang: /* System Scheme */

2011-08-21T16:14:22Z

System Scheme

← Older revision		Revision as of 16:14, 21 August 2011
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	==System Scheme==		==System Scheme==
-	[[File:MicrogridScheme.png \|thumb\|right\|~~500px~~\|Microgrid for a community]]	+	[[File:MicrogridScheme.png \|thumb\|right\|350px\|Microgrid for a community]]
	The Microgrid is invested and shared by a cluster of residents. According to ratios of investment, residents subscribe certain amount of power from the microgrid with low price for unpaid investment cost, fuel cost and management cost. There are three types of flow in the microgrid: electric power flow, thermal power flow and information flow. The electric power is powered by both utility grid and internal hybrid energy generated by wind turbine, micro CHP systems, and battery discharging. Micro CHP is a smaller size CHP for residential generation. For general consideration, some houses in the community are equipped with micro CHP systems, whereas others are not. For the latter, thermal energy can only be generated by electric heat pump. The thermal energy will be stored in the form of hot water in the tank and can be consumed in the future. VRB is chosen to store surplus wind energy and discharge when the wind energy is insufficient for the demand to reduce the energy consumption cost.		The Microgrid is invested and shared by a cluster of residents. According to ratios of investment, residents subscribe certain amount of power from the microgrid with low price for unpaid investment cost, fuel cost and management cost. There are three types of flow in the microgrid: electric power flow, thermal power flow and information flow. The electric power is powered by both utility grid and internal hybrid energy generated by wind turbine, micro CHP systems, and battery discharging. Micro CHP is a smaller size CHP for residential generation. For general consideration, some houses in the community are equipped with micro CHP systems, whereas others are not. For the latter, thermal energy can only be generated by electric heat pump. The thermal energy will be stored in the form of hot water in the tank and can be consumed in the future. VRB is chosen to store surplus wind energy and discharge when the wind energy is insufficient for the demand to reduce the energy consumption cost.
	<br\>		<br\>

Bnjiang: /* System Scheme */

2011-08-21T16:13:59Z

System Scheme

← Older revision		Revision as of 16:13, 21 August 2011
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	*Shared cost-led micro CHP generation management for cost reduction.		*Shared cost-led micro CHP generation management for cost reduction.
	*Environment adaptive VRB discharging management based on [http://en.wikipedia.org/wiki/Reinforcement_learning Reinforcement Learning] for cost reduction considering stochastic elements.		*Environment adaptive VRB discharging management based on [http://en.wikipedia.org/wiki/Reinforcement_learning Reinforcement Learning] for cost reduction considering stochastic elements.
-	~~<br\>~~
-	~~<br\>~~

	==System Models==		==System Models==

Bnjiang: /* System Scheme */

2011-08-21T16:13:22Z

System Scheme

← Older revision		Revision as of 16:13, 21 August 2011
Line 12:		Line 12:
	[[File:MicrogridScheme.png \|thumb\|right\|500px\|Microgrid for a community]]		[[File:MicrogridScheme.png \|thumb\|right\|500px\|Microgrid for a community]]
	The Microgrid is invested and shared by a cluster of residents. According to ratios of investment, residents subscribe certain amount of power from the microgrid with low price for unpaid investment cost, fuel cost and management cost. There are three types of flow in the microgrid: electric power flow, thermal power flow and information flow. The electric power is powered by both utility grid and internal hybrid energy generated by wind turbine, micro CHP systems, and battery discharging. Micro CHP is a smaller size CHP for residential generation. For general consideration, some houses in the community are equipped with micro CHP systems, whereas others are not. For the latter, thermal energy can only be generated by electric heat pump. The thermal energy will be stored in the form of hot water in the tank and can be consumed in the future. VRB is chosen to store surplus wind energy and discharge when the wind energy is insufficient for the demand to reduce the energy consumption cost.		The Microgrid is invested and shared by a cluster of residents. According to ratios of investment, residents subscribe certain amount of power from the microgrid with low price for unpaid investment cost, fuel cost and management cost. There are three types of flow in the microgrid: electric power flow, thermal power flow and information flow. The electric power is powered by both utility grid and internal hybrid energy generated by wind turbine, micro CHP systems, and battery discharging. Micro CHP is a smaller size CHP for residential generation. For general consideration, some houses in the community are equipped with micro CHP systems, whereas others are not. For the latter, thermal energy can only be generated by electric heat pump. The thermal energy will be stored in the form of hot water in the tank and can be consumed in the future. VRB is chosen to store surplus wind energy and discharge when the wind energy is insufficient for the demand to reduce the energy consumption cost.
-		+	<br\>
		+	<br\>
	The management of the microgrid is based on hierarchical optimization and bill balance. The hierachical optimization is based on centralized and distributed agents and has 3 steps.		The management of the microgrid is based on hierarchical optimization and bill balance. The hierachical optimization is based on centralized and distributed agents and has 3 steps.
	*Dynamic residential DR for energy consumption cost reduction, high user's convenience rate and user's preference adaption.		*Dynamic residential DR for energy consumption cost reduction, high user's convenience rate and user's preference adaption.
	*Shared cost-led micro CHP generation management for cost reduction.		*Shared cost-led micro CHP generation management for cost reduction.
-	*Environment adaptive VRB discharging management for cost reduction considering stochastic elements.	+	*Environment adaptive VRB discharging management based on [http://en.wikipedia.org/wiki/Reinforcement_learning Reinforcement Learning] for cost reduction considering stochastic elements.
		+	<br\>
	<br\>		<br\>

Bnjiang: /* Environment Adaptive VRB Management */

2011-08-21T16:10:30Z

Environment Adaptive VRB Management

← Older revision		Revision as of 16:10, 21 August 2011
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	b = \frac{{\left( {{R_G}(k) - {R_B}} \right) \times \min \left( {{{E'}_{INSF}}(k),{E_{DCH}}(k)} \right)}}{{\left( {{R_{GMAX}}(k) - {R_B}} \right) \times {E_{DCH}}(k)}}		b = \frac{{\left( {{R_G}(k) - {R_B}} \right) \times \min \left( {{{E'}_{INSF}}(k),{E_{DCH}}(k)} \right)}}{{\left( {{R_{GMAX}}(k) - {R_B}} \right) \times {E_{DCH}}(k)}}
	</math>		</math>
		+
		+	In this step, the online reinforcement learning is realized by using [http://en.wikipedia.org/wiki/Q-learning Q-learning] algorithm.

	=People=		=People=
	=Publications=		=Publications=

Bnjiang: /* Dynamic Residential DR */

2011-08-21T16:07:02Z

Dynamic Residential DR

← Older revision		Revision as of 16:07, 21 August 2011
Line 24:		Line 24:
	[[File:PRFunction.png\|thumb\|right\|300px\|Reward/Penalty function]]		[[File:PRFunction.png\|thumb\|right\|300px\|Reward/Penalty function]]
	Electricity demands are represented as schedulable and fixed energy consumption tasks. Dynamic DR focuses on the schedulable load. Schedulable tasks have their own convenience rate distributions, which reflect the satisfaction users can get when tasks are executed at different time.		Electricity demands are represented as schedulable and fixed energy consumption tasks. Dynamic DR focuses on the schedulable load. Schedulable tasks have their own convenience rate distributions, which reflect the satisfaction users can get when tasks are executed at different time.
-	~~<br\>~~	+
-	~~<br\>~~	+
	The model of dynamic DR consists of dynamic task array, external information and task convenience rate function as inputs, and generate task scheduling as outputs. After scheduling, only tasks scheduled in current decision period are executed. Others will be rescheduled in the next decision period with updated external information.		The model of dynamic DR consists of dynamic task array, external information and task convenience rate function as inputs, and generate task scheduling as outputs. After scheduling, only tasks scheduled in current decision period are executed. Others will be rescheduled in the next decision period with updated external information.
-	~~<br\>~~	+
-	~~<br\>~~	+
	The convenience rate function comprises Basic function and Reward/Penalty function. The basic function is used for initialization. The Reward/Penalty function serves as agents’ reaction towards user’s interference and is used to update the convenience rate function dynamically. The user may attempt to change task execution time scheduled by the agent. The new execution time is considered as the new comfort time for the user and will be given a maximum convenience rate increase. The old scheduled time will be penalized by decreasing its convenience rate with the same value.		The convenience rate function comprises Basic function and Reward/Penalty function. The basic function is used for initialization. The Reward/Penalty function serves as agents’ reaction towards user’s interference and is used to update the convenience rate function dynamically. The user may attempt to change task execution time scheduled by the agent. The new execution time is considered as the new comfort time for the user and will be given a maximum convenience rate increase. The old scheduled time will be penalized by decreasing its convenience rate with the same value.
-	~~<br\>~~	+
-	~~<br\>~~	+
	DR agent will optimize the following cost function considering both energy consumption cost and user's convenience:		DR agent will optimize the following cost function considering both energy consumption cost and user's convenience:
	<math>\min \qquad J = \frac{{C_{{k_n}}^{}}}{{U_{{k_n}}^{}}} + (I - \sum\limits_{i = 1}^I {s_i^{{k_n}}} ){P_T}		<math>\min \qquad J = \frac{{C_{{k_n}}^{}}}{{U_{{k_n}}^{}}} + (I - \sum\limits_{i = 1}^I {s_i^{{k_n}}} ){P_T}