On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens

The electricity power market is well known for its highly volatile nature due to its innate variability characteristic of demand and the absence of practical bulk storage at reasonable cost. Any discordance between rapid fluctuation in wholesale prices and near flat retail prices not only incurs economic inefficiency in terms of social welfare, but also creates price-inelastic wholesale demand which severely exacerbates the volatility of wholesale electricity prices. While the market has a fundamental dynamic nature, the behavioral aspect of power consumption in response to price changes is not well understood. This necessitate to develop a empirical modeling methodology of demand which can potentially provide practical insights into demand response. In the former part of this work, we focus on dynamic aspect of demand response in Chapter 2. We first show that (i) demand is well responsive to outlier high price surges, and (ii) demand response can incur a certain amount of delay. Examining further data, it appears that demand is responsive to anticipated prices. This is in conformity with our previous observations on the inertia of demand, and testing the hypothesis that demand actually responds to anticipated prices rather than actual real time prices is an important next step. While it is impractical to obtain a particular individual’s own price prediction, We propose to test the hypothesis with day-ahead electricity prices (DAP). In addition, as an initial step toward the derivation of a quantitative model of electricity load and price, we propose a model of “appliance” usage as a representative basic component of electricity load. In the latter part of this work, we investigate more fundamental aspect of data-centric modeling in Chapter 3. First, we show the limitation of pure data-centric modeling strategy by proving that having a perfect knowledge on the joint distribution on price and load does not identify the load behavior in response to price. As it turns out that the causal structure of the variables of interest is the central matter that determines load behavior identifiability, we derive a minimal identifiable causal structure of demand response from the preexisting economic theories. Based on the discovered causal structure, we propose a minimal Bayesian model representation called “stochastic neuron” which connects machine learning technique to demand response modeling. We show that a stochastic neuron is an explainable tool as expressive as an ordinary neural network, and well extends the arguments from “appliance” usage model

OF@TEIN: An OpenFlow-enabled SDN Testbed over International SmartX Rack Sites

In this paper, we will discuss our on-going effort for OF@TEIN SDN(Software-Defined Networking) testbed, which currently spans over Korea and fiveSouth-East Asian (SEA) collaborators with internationally deployed OpenFlowenabledSmartX Racks

On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens

The electricity power market is well known for its highly volatile nature due to its innate variability characteristic of demand and the absence of practical bulk storage at reasonable cost. Any discordance between rapid fluctuation in wholesale prices and near flat retail prices not only incurs economic inefficiency in terms of social welfare, but also creates price-inelastic wholesale demand which severely exacerbates the volatility of wholesale electricity prices. While the market has a fundamental dynamic nature, the behavioral aspect of power consumption in response to price changes is not well understood. This necessitate to develop a empirical modeling methodology of demand which can potentially provide practical insights into demand response. In the former part of this work, we focus on dynamic aspect of demand response in Chapter 2. We first show that (i) demand is well responsive to outlier high price surges, and (ii) demand response can incur a certain amount of delay. Examining further data, it appears that demand is responsive to anticipated prices. This is in conformity with our previous observations on the inertia of demand, and testing the hypothesis that demand actually responds to anticipated prices rather than actual real time prices is an important next step. While it is impractical to obtain a particular individual’s own price prediction, We propose to test the hypothesis with day-ahead electricity prices (DAP). In addition, as an initial step toward the derivation of a quantitative model of electricity load and price, we propose a model of “appliance” usage as a representative basic component of electricity load. In the latter part of this work, we investigate more fundamental aspect of data-centric modeling in Chapter 3. First, we show the limitation of pure data-centric modeling strategy by proving that having a perfect knowledge on the joint distribution on price and load does not identify the load behavior in response to price. As it turns out that the causal structure of the variables of interest is the central matter that determines load behavior identifiability, we derive a minimal identifiable causal structure of demand response from the preexisting economic theories. Based on the discovered causal structure, we propose a minimal Bayesian model representation called “stochastic neuron” which connects machine learning technique to demand response modeling. We show that a stochastic neuron is an explainable tool as expressive as an ordinary neural network, and well extends the arguments from “appliance” usage model

Trans-Anethole Alleviates Trimethyltin Chloride-Induced Impairments in Long-Term Potentiation

Trans-anethole is an aromatic compound that has been studied for its anti-inflammation, anticonvulsant, antinociceptive, and anticancer effects. A recent report found that trans-anethole exerted neuroprotective effects on the brain via multiple pathways. Since noxious stimuli may both induce neuronal cell injury and affect synaptic functions (e.g., synaptic transmission or plasticity), it is important to understand whether the neuroprotective effect of trans-anethole extends to synaptic plasticity. Here, the effects of trimethyltin (TMT), which is a neurotoxic organotin compound, was investigated using the field recording method on hippocampal slice of mice. The influence of trans-anethole on long-term potentiation (LTP) was also studied for both NMDA receptor-dependent and NMDA receptor–independent cases. The action of trans-anethole on TMT-induced LTP impairment was examined, too. These results revealed that trans-anethole enhances NMDA receptor-dependent and -independent LTP and alleviates TMT-induced LTP impairment. These results suggest that trans-anethole modulates hippocampal LTP induction, prompting us to speculate that it may be helpful for improving cognitive impairment arising from neurodegenerative diseases, including Alzheimer’s disease