IEEE Power & Energy Magazine - March/April 2018 - 80

The game of Go, also called Weiqi or Baduk,
is a strategic board game that originated in China
more than 2,500 years ago.
in the previous wholesale market bidding case, players
simultaneously submitted their bids to the market operator to compete for market share and were weighted equally
during the trading. however, the power transaction at the
distribution system can be modeled as a leader-follower
Stackelberg game, where a hierarchy exists among the players. in this case, a central controller such as a distribution
system operator or a load aggregator, first releases a retail
price to stimulate the local der response with the aim of
maximizing its own economic benefit and is referred to as
the leader. then, der owners can decide their dispatch
schedules based on the observation of the price signal, also
with the goal of maximum profit. they are categorized as
the followers since their behaviors are dependent on the
price set by the leader.
Both sides face uncertainties during the game. the leader
lacks the facility information of the demand side, e.g., capacities of dGs, dr customer consumption habits, and numbers
and types of interruptible home appliances. der owners
have to think over the potential future price fluctuations when
scheduling their dispatches, which is a multistage decisionmaking process. the uncertainties result in a huge search
space to locate systemwide equilibrium status, where a dnn
technique can be used to tackle the problem.
in the sense of deep learning, the policy network of the
central controller is the retail price, and the policy network
of der owners is the dispatch schedule. to establish a
policy network for the central controller via dnn training,
the input will be the amount of power purchased by or sold
from der owners, and the output is the retail price. as for
the der owners, the input to the dnn will be the price information, and the output is the dispatch schedule. For both parties, the value network is the economic profit. considering a
multiperiod or multistage power transaction, an mctS can be
applied based on the above well-trained policy network and
value network to better evaluate the strategies at current
stages based on their potential future consequences.

Coordinated Management
of Plug-in Electric Vehicles
recently, the large-scale penetration of plug-in electric
vehicles (phevs) into the distribution system has turned
into a realistic vision. however, the growing intermittent
ev demand may congest power delivery systems and overload the substation transformers, which exacerbates system
operational risks and brings additional costs if there is no
coordinated control.
80

ieee power & energy magazine

Similar to the wholesale market bidding problem, the
optimal management of ev fleets can be formulated as a
noncooperative game, with each ev user (or its representative agents or aggregators) competing for the limited system
power transmission capacity to reach the desired energy level
with the lowest cost. the unique feature of this problem is
that each ev user can shift between the role of power generator and power consumer via discharging or charging activity,
which leads to a larger strategy space. this is also a multistage decision-making process since an ev battery has a
fixed capacity and the energy conservation level at the current stage is affected by the operations at previous stages.
modeling the optimal ev user behaviors can be implemented via an alphaGo-like algorithm as well. here, the policy network is the ev user's strategies (charging, discharging,
and idle), and the value network is the total cost of one cycle,
including the charging cost and system risk cost. Both networks can be trained based on the past ev user behaviors
and can be further reinforced via self-plays. at each stage, a
tree search can be implemented to evaluate all the possible
strategies by predicting future consequences, and the strategy
with the highest evaluation can be selected. While individual
ev owners may not be motivated to implement this on their
own, it is more likely that their representatives or aggregators
may determine the best strategy for charging, discharging or
being idle.

Potential Applications
in Search-Based Studies
Unlike these gaming problems, some studies in power systems only involve one single entity of study, which means no
interaction with other entities. in such problems, the system
state is a key element. the space of the system state can be
tremendous, involving multiple scenarios, multiple stages
(time intervals), or both. For instance, typical problems for
the multiscenario cases include security assessment under
multiple renewable energy scenarios and fault location identification under different operating conditions. also, a cascading failure assessment can be classified as a multistage
problem, while the rolling optimization of renewable energy
dispatch is a typical instance involving both multiscenarios
and multistages.
during real-time monitoring and operation, a huge system
state space raises the challenge of computation and communication. hence, similar to the search for the most favorable
move in the Go game, dnn and mctS techniques can also
make these problems tractable by identifying the most critical
march/april 2018



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - March/April 2018

Contents
IEEE Power & Energy Magazine - March/April 2018 - Cover1
IEEE Power & Energy Magazine - March/April 2018 - Cover2
IEEE Power & Energy Magazine - March/April 2018 - Contents
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IEEE Power & Energy Magazine - March/April 2018 - Cover3
IEEE Power & Energy Magazine - March/April 2018 - Cover4
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