Electricity prices and the end user net load vary with time. Electricity consumers equipped with energy storage devices can perform energy arbitrage, i.e., buy when energy is cheap or when there is a deficit of energy, and sell it when it is expensive or in excess, taking into account future variations in price and net load. Net metering policies indicate that many of the utilities apply a {customer selling} rate lower than or equal to the retail {customer buying rate} in order to compensate excess energy generated by end users. In this paper, we formulate the optimal control problem for an end user energy storage device in presence of net metering. We propose a computationally efficient algorithm, with worst case run time complexity of quadratic in terms of number of samples in lookahead horizon, that computes the optimal energy ramping rates in a time horizon. The proposed algorithm exploits the problem's piecewise linear structure and convexity properties for the \textit{discretization} of optimal Lagrange multipliers. The solution has a \textit{threshold-based structure} in which optimal control decisions are independent of past or future price as well as of net load values beyond a certain time horizon, defined as a \textit{sub-horizon}. Numerical results show the effectiveness of the proposed model and algorithm. Furthermore, we investigate the impact of forecasting errors on the proposed technique. We consider an Auto-Regressive Moving Average (ARMA) based forecasting of net load together with the Model Predictive Control (MPC). We numerically show that adaptive forecasting and MPC significantly mitigate the effects of forecast error on energy arbitrage gains.
A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time was obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was performed by multi-objective optimization in the presence of a battery storage system and…
We formulate the optimal energy arbitrage problem for a piecewise linear cost function for energy storage devices using linear programming (LP). The LP formulation is based on the equivalent minimization of the epigraph. This formulation considers ramping and capacity constraints, charging and discharging efficiency losses of the storage, inelastic consumer load and local renewable generation in presence of net-metering which facilitates selling of energy to the grid and incentivizes consumers to install renewable generation and energy storage. We consider the case where the consumer loads, el…
Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provi…
Sharing economy has become a socio-economic trend in transportation and housing sectors. It develops business models leveraging underutilized resources. Like those sectors, power grid is also becoming smarter with many flexible resources, and researchers are investigating the impact of sharing resources here as well that can help to reduce cost and extract value. In this work, we investigate sharing of energy storage devices among individual households in a cooperative fashion. Coalitional game theory is used to model the scenario where utility company imposes time-of-use (ToU) price and net m…
Real-time Building Energy Storage Scheduling under Electrical Load Uncertainty: A Dynamic Markov Decision Process Approach with Comprehensive Analysis of Different Pricing Policies
With the proliferation of advanced metering infrastructure (AMI), more real-time data is available to electric utilities and consumers. Such high volumes of data facilitate innovative electricity rate structures beyond flat-rate and time-of-use (TOU) tariffs. One such innovation is real-time pricing (RTP), in which the wholesale market-clearing price is passed directly to the consumer on an hour-by-hour basis. While rare, RTP exists in parts of the United States and has been observed to reduce electric bills. Although these reductions are largely incidental, RTP may represent an opportunity fo…
In this paper, we aim to leverage peer to peer (P2P) transactive energy framework for optimal control of rooftop or neighborhood solar power with battery electric storage systems (BESS). Here we propose that the multiple neighboring customers would interconnect to form a community DC grid while still being connected to the main utility grid. The proposed infrastructure would (i) increase the resilience of the customers in case the utility grid becomes unavailable, (ii) reduce AC/DC conversion losses since a majority of the DER generation is in DC, and (iii) with the help of BESS, the customers…
In this paper, we propose a high-level Stochastic steady-state model to analyze the value of co-located energy storage systems for wind power producers that participate in an electricity market through Forward or Day Ahead contracts. In particular, we try to find optimal sizing and contracting and stationary operating policies for profit maximization in the long-run. We obtain a stylized model calibrated to actual wind power production and electricity wholesale price data that allows us to asses the value of storage size and perform sensitivity analysis on key parameters such as contract price…
This paper discusses the functional stability of closed-loop Markov Chains under optimal policies resulting from a discounted optimality criterion, forming Markov Decision Processes (MDPs). We investigate the stability of MDPs in the sense of probability measures (densities) underlying the state distributions and extend the dissipativity theory of Economic Model Predictive Control in order to characterize the MDP stability. This theory requires a so-called storage function satisfying a dissipativity inequality. In the probability measures space and for the discounted setting, we introduce new …
The increasing adoption of smart meters introduces growing concerns about consumer privacy risks stemming from high resolution metering data. To counter these risks, there have been various works in actively shaping the grid-visible energy consumption profile using controllable loads such as energy storage systems (ESSs) and flexible consumer loads. In this paper, we compare the use of flexible thermal-based consumer loads (FTLs) against ESSs for consumer privacy protection. By first assuming ideal conditions, and subsequently bringing them closer to reality, the limitations of using FTLs for …
The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OP…
Large scale renewable energy integration is being planned for multiple power grids around the world. To achieve secure and stable grid operations, additional resources/reserves are needed to mitigate the inherent intermittency of renewable energy sources (RES). In this paper, we present formulations to understand the effect of fast storage reserves in improving grid reliability under different cost functions. Our formulations and solution schemes not only aim to minimize imbalance but also maintain state-of-charge (SoC) of storage. In particular, we show that accounting for system response due…
Decarbonization of the electricity sector is one of the major measures in slowing down the pace of climate change. In this paper, we analyze the impacts of energy storage systems (ESS) and interannual uncertainty of variable renewable energy (VRE) on power system decarbonization in 2050. We perform capacity expansion optimization based on technology cost projections and CO2 emission restrictions using 11 years of VRE and load data in Italy's power system, with a particular focus on the role of ESS and its duration. We also explore capacity expansion optimization based on multiple-year vs. sing…
Sharing economy has disrupted many industries. We foresee that electricity storage systems could be the enabler for sharing economy in electricity sector, though its implementation is a delicate task. Unlike in the 2-tier Time-of-Use (ToU) pricing, where greedy arbitrage policy can achieve the maximal electricity bill savings, most existing ToU schemes consist of multiple tiers, which renders the arbitrage challenging. The difficulty comes from the hedging against multiple tiers and the coupling between the decisions across the day. In this work, we focus on designing the energy sharing mechan…
The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC…
Smart grid technology has been recognized as a promising solution for the next-generation energy efficient electric power systems to mitigate energy crisis. Smart grid provides highly consistent and reliable services, efficient energy management practices, smart metering integration, automation and precision decision support systems and self-healing facilities. The smart power grid introduces a sensing, monitoring, and control system that provides end users with the cost of energy at any moment through real-time pricing. The classical power system operation has no control over the loads except…
Norwegian University of Science and Technology (NTNU)（挪威科技大学）
In this paper, we are interested in optimal control problems with purely economic costs, which often yield optimal policies having a (nearly) bang-bang structure. We focus on policy approximations based on Model Predictive Control (MPC) and the use of the deterministic policy gradient method to optimize the MPC closed-loop performance in the presence of unmodelled stochasticity or model error. When the policy has a (nearly) bang-bang structure, we observe that the policy gradient method can struggle to produce meaningful steps in the policy parameters. To tackle this issue, we propose a homoto…
With the increased amount of volatile renewable energy sources connected to the electricity grid, and the phase-out of fossil fuel based power plants, there is an increased need for frequency regulation. On the demand side, frequency regulation services can be offered by buildings or districts that are equipped with electric heating or cooling systems, by exploiting their thermal inertia. Existing approaches for tapping into this potential typically rely on dynamic building models, which in practice can be challenging to obtain and maintain. As a result, practical implementations of such syste…
The intermittent nature of renewable energy resources such as wind and solar causes the energy supply to be less predictable leading to possible mismatches in the power network. To this end, hydrogen production and storage can provide a solution by increasing flexibility within the system. Stored hydrogen can either be converted back to electricity or it can be used as feed-stock for industry, heating for built environment, and as fuel for vehicles. This research examines the optimal strategies for operating integrated energy systems consisting of renewable energy production and hydrogen stora…
Approximate Dynamic Programming for Degradation-aware Market Participation of Battery Energy Storage Systems: Bridging Market and Degradation Timescales
