3,717 research outputs found
Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts
We develop a stochastic equilibrium model for an electricity market with
asymmetric renewable energy forecasts. In our setting, market participants
optimize their profits using public information about a conditional expectation
of energy production but use private information about the forecast error
distribution. This information is given in the form of samples and incorporated
into profit-maximizing optimizations of market participants through chance
constraints. We model information asymmetry by varying the sample size of
participants' private information. We show that with more information
available, the equilibrium gradually converges to the ideal solution provided
by the perfect information scenario. Under information scarcity, however, we
show that the market converges to the ideal equilibrium if participants are to
infer the forecast error distribution from the statistical properties of the
data at hand or share their private forecasts
Developing a simulator for the Greek electricity market
Following the liberalization of the Greek electricity market, the Greek Regulatory Authority for Energy (RAE) undertook the design and implementation of a simulator for the wholesale market and its interactions with the Natural Gas Transportation System. The simulator consists of several interacting modules representing all key market operations and dynamics including (i) day-ahead scheduling based on bids of market participants, (ii) natural gas system constraints, (iii) unplanned variability of loads and available capacity driven either by uncertain stochastic outcomes or deliberate participant schedule deviations, (iv) real time dispatch, and (v) financial settlement of day ahead and real time schedule differences. The modules are integrated into one software package capable of simulating all market dynamics, deliberate or probabilistic, and their interactions across all relevant time scales. The intended use of the simulator is to elaborate on and allow RAE to investigate the impact of participant decision strategies on market outcomes. The ultimate purpose is to evaluate the effectiveness of Market Rules, whether existing or contemplated, in providing incentives for competitive behaviour and in discouraging gaming and market manipulation. This paper describes the development of the simulator relative to the current Greek Electricity Market Design and key contemplated revisions.simulation; regulatory policy; electricity markets; market design;
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage
We propose and experimentally validate a control strategy to dispatch the
operation of a distribution feeder interfacing heterogeneous prosumers by using
a grid-connected battery energy storage system (BESS) as a controllable element
coupled with a minimally invasive monitoring infrastructure. It consists in a
two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute
average power consumption trajectory for the next day of operation (called
\emph{dispatch plan}) is determined, and intra-day/real-time operation, where
the mismatch with respect to the \emph{dispatch plan} is corrected by applying
receding horizon model predictive control (MPC) to decide the BESS
charging/discharging profile while accounting for operational constraints. The
consumption forecast necessary to compute the \emph{dispatch plan} and the
battery model for the MPC algorithm are built by applying adaptive data driven
methodologies. The discussed control framework currently operates on a daily
basis to dispatch the operation of a 20~kV feeder of the EPFL university campus
using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
Regulated Expansion of Electricity Transmission Networks: The Effects of Fluctuating Demand and Wind Generation
We study the performance of different regulatory approaches for the expansion of electricity transmission networks in the light of realistic demand patterns and fluctuating wind power. In particular, we are interested in the relative performance of a combined merchant-regulatory mechanism compared to a cost-based and a merchant-like approach. In contrast to earlier research, we explicitly include both an hourly time resolution and fluctuating wind power, which allows representing demand in a very realistic way. This substantially increases the real-world applicability of results compared to previous analyses, which were based on simplifying assumptions. We show that a combined merchant-regulatory regulation, which draws on a cap over the two-part tariff of the Transco, leads to welfare outcomes far superior to the modeled alternatives. This result proves to be robust over a range of different cases and sensitivity analyses. We also find that the intertemporal rebalancing of the two-part tariff carried out by the Transco so as to expand the network is such that the fixed tariff part turns out to be relatively large compared to extension costs.Electricity, Regulation, Transmission Expansion, Wind Power
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