

TRANSACTIONS ON ENVIRONMENT AND ELECTRICAL ENGINEERING ISSN 2450-5730 Vol 1, No 3 (2016)

Abstract— Demand side response enables cost optimization for

energy systems and industrial consumers. In many countries, it is
not widely used because of implementation complexity. One of the
solutions for applying demand side response is industrial process
scheduling according to the energy market needs. From the energy
system point of view, process scheduling implies load scheduling.
The aim of this paper is to provide a solution for load scheduling
by implementing value stream mapping, which is a
straightforward enough for production management. Decision
makers in the industry should have a clear understanding about
positive effect from load scheduling and its effect to production
outcome and process availability. Value stream mapping is a well-
known process optimization tool from lean production philosophy.
The aim of value stream mapping is to shorten the lead time of
industrial processes and to reduce the intermediate stock amounts.
By complementing value stream map with process energy intensity
and energy stored in intermediate stocks, we can promote load
scheduling possibilities. Our methodology provides a tool that is
understandable and traceable for industry-minded decision
makers. Finally, we present a real life test example for the new
methodology, which is based on the production process of a district
heating plant.

Index Terms— demand response, energy storage, load
management, load scheduling, value stream mapping

I. INTRODUCTION
OAD scheduling (LS) as part of demand side response
(DSR) must meet the needs of industry. One of the effects

for the industry appears when load consumption is shifted from
periods of high electricity price to those of low price. As a
result, cost savings can be achieved by means of reduced
consumer demand in high price periods. This requires better
production planning, which is related to production
management.

Previous DSR studies have resulted in the following: static
Stackelberg game theory for voluntary load curtailment
programs [1]; numerical calculation method for DSR when a
battery energy storage system (BESS) is utilized [2]; solutions
for DSR by means of automatic lighting [3]; DSR with micro-
CHP systems [4]; an overview for DSR methods in high

This research was supported by the Estonian Centre of Excellence in Zero
Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant
2014-2020.4.01.15-0016 funded by the European Regional Development Fund.

consumption industries and examples of market tools that
support DSR [5]. In [6] an automated complex system for LS in
industry is described, which takes into account stock
restrictions, maintenance schedules, and crew management. All
the necessary inputs are analyzed with a fuzzy/expert-based
system combined with an optimization module. As a result, the
system is able to identify whether and how much the industrial
plant can participate in a DSR event. In [7] and [8] DSR is
addressed as a part of the following main load shaping
strategies:

a) conservation - energy saving is achieved through
static methods;

b) load growth - energy consumption is increased when
an energy system has surplus energy production;

c) valley filling - load is increased through the off-peak
periods or keeping stable consumption;

d) peak clipping - energy consumption is decreased in
peak periods;

e) load shifting - peak consumption is shifted from peak
periods to non-peak periods;

f) seasonal load reduction - annual energy peaks are
reduced.

LS is used mainly in “load shifting” strategy e; however, in
some cases, “valley filling” strategy can be utilized as well. To
use the LS, an industry must have the following one or several
DSR options [8]: cooling equipment with cooling storage,
heating equipment with heat storage, dual fuel systems that can
operate either on electricity or on an alternative fuel,
discretionary loads and process equipment that can be shifted
during a short period or material handling equipment with
storage possibilities (silos, stock, etc.). This paper focuses on
the last option by looking at process as a whole in order to find
LS solutions. In addition, it provides a method applicable in
industry for outlining the possibilities with LS as a part of DSR
by using value stream mapping (VSM). VSM is applicable in

R. Melsas, A. Rosin and I. Drovtar are with the Department of Electrical
Engineering, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn,
Estonia (e-mail: raivomelsas@gmail.com, argo.rosin@ttu.ee,
imre.drovtar@gmail.com).

Value Stream Mapping for Evaluation of Load
Scheduling Possibilities in a District Heating

Plant
Raivo Melsas, Argo Rosin and Imre Drovtar

various ways. Originating from Toyota Production Systems [9],
it was further elaborated and adjusted to find solutions for
different problems in the production process. For example,
VSM is used to solve quality problems [10]. This paper
elaborates on VSM. Our proposal is to use it for indicating a
possibility to shift an electrical load from a high price period to
a low price period and utilize an intermediate stock for energy
storage [11], [12], [13]. This paper will provide a
straightforward solution for the industry in order to apply LS
effectively and which is easily applicable.

II. II. ENERGY PRICE AS A DRIVER FOR LOAD
SHIFTING

LS can yield an economic effect under rational consideration.
Cost reduction can be achieved by taking advantage of energy
price fluctuations during a day. It is reasonable to have demand
response implemented in countries where an electricity pool
exists and hourly based spot prices are known for a short period
ahead. As a result, industries can plan their production
according to the spot price. Fig. 1 shows an average 24-hour
electricity market spot price in Estonia in 2014 [14]. As can be
seen, electricity spot price is typically higher from 07:00 A.M.
to 8:00 P.M. In general, there is at least 10-euro price difference
during a day and a night. Considering the price peak and dip
approximately 20-euro difference per MWh exists during a day.
On average, price difference during a day is 15-euro MWh.

However the consideration above includes only electricity
price; in addition, there are some fluctuations in grid service
price as well. From 12:00 A.M. to 8:00 A.M. (11:00 P.M. to
7:00 A.M. in winter time), the grid service price is lower. This
period is not overlapping 100% with a low spot price period.
We will call this period (12:00 A.M. to 8:00 A.M.) as the Low
Price Period (LPP) and the other period during a day as the High
Price Period (HPP). Grid tariffs depend on grid connection
voltage level and connection capacity (amps). In the following,
we will describe one example to examine the daily price
difference for the industry in Estonia.

At substantial electricity consumption, an industry is usually
connected into a middle voltage (MV) grid. In that case,
Estonian grid service price is 14.5 euros per MWh from 8:00
A.M. to 24:00 A.M. and during LPP 8.3 euros per MWh [15].
For some customers, no time difference is applied; the price for
grid services is constant in time- 12 euros per MWh. Fig. 1
shows both the grid price and the spot price fluctuations.

In 2014, an average electricity spot price for LPP was 29.5
euros per MWh and with grid price fluctuations it amounted to
37.8 euros per MWh, which we call as the Low Price Period
Price (LPPP). In 2014, an average HPP spot price was 40.9 and
together with grid tariff, the average number was 55.1 euros per
MWh, which we call as the High Price Period Price (HPPP).
For clarity, 55.1 is an average found on hourly bases, i.e. spot
price + grid price.

Based on the data provided, we can calculate potential
savings for industry under LS. Potential cost saving is 31%,
which is calculated by (1):

.
HPPP

LPPPHPPP
CS

−
= (1)

In general, it can be concluded that grid tariff fluctuation has

an important role in LS in Estonia, as the average difference in
the spot price between HPP and LPP was 11.4 euros per MWh
and grid tariff will add extra for the difference between HPPP
and LPPP, according to [15], tariff depends on the grid
connection parameters.

The shape of the electricity spot price in Fig. 1 can be
considered as a typical shape of the daily demand of electricity
as well; a similar shape of demand can be found in various
places, e.g., even in South Africa [16]. Thus, DSR has a positive
impact to overall efficiency to energy systems, not only to an
industry itself.

III. IMPROVEMENT OF VALUE STREAM MAPPING
METHODOLOGY FOR EVALUATION OF LOAD

SCHEDULING POSSIBILITIES

A. Load Scheduling at Intermediate Storage Use in
Production

In the following, LS possibilities are examined for
intermediate storage use as an energy saving unit, to enable
shifting of energy intense production from HPP to LPP. In LS,
it is important to understand energy intensive production units
and their overall role in the production. Methods from lean
philosophy [LP] can be used here. We propose to improve the
value stream mapping (VSM) methodology with LS principles.
Today LP is a leading production management philosophy.
VSM is used to plan production as efficiently as reasonably
possible. Energy intensity can be added in a process as
additional information for a VSM, which will give a good
overview about the possibilities in energy saving. Fig. 2 gives
an overall picture of a typical VSM, elaborated with process
energy intensity and amount of energy stored in the production
intermediate storage.

Fig. 1. Average Estonian 2014 electricity spot price during a day.

1 3 5 7 9 11 13 15 17 19 21 23

Pr
ic

e
(E

U
R

/M
W

Hours in a day

spot+grid price on HV line spot price

As VSM gives an overview of the production planning and

can additionally give information about energy intensity, the
possibility of the LS should be studied in detail. From VSM, we
will know if an energy intense process is at the same time a
bottleneck in production. If it is not true, then the conclusion is
that this process is not 100% utilized in time and LS can be
implemented without increasing process capacity. Alternatively
costs for increasing process capacity (CPC) need to be
calculated.

Next, we focus on the storage. If production is shifted in time,
storage volume can increase as compared to the state without
LS utilized. Storage increase may need additional investments,
which should also be considered as costs for storage (CS). Also,
costs of the process (CP) itself for LS should be taken into
account. As an example CP related to LS can be an increase in
labor costs due to night shifts. Finally, if the costs related to LS
are lower than a possible income from LS (ILS), the LS can be
implemented in the industrial process. This criterion is given in
the following (2):

ILS<CP+CS+CPC . (2)

B. Methodology for LS with VSM.
The main initial goal of LP and VSM was to reduce lead time

(L/T), i.e, the time it takes to move a produced piece all the way
in the process or production from start until to the end. As a
result, many costs or wastes, as defined in LP, will be reduced.
As shown in (2), income from LS should be higher than costs
related to it because LS can increase the intermediate stock and
due to that it has a negative effect on the L/T as well. VSM is a
tool that will help to find the processes which have a reasonable
effect and income. To achieve that, cost saving potential must
be estimated. In section II, we showed a possible gain from LS
in Estonia. The next step is to define the energy consumption of
the processes. It is useful to combine that with VSM. If a
company already has VSM, then energy consumption should be
added into the VSM. VSM will highlight important information

to be considered for LS. Most importantly, the following
information must be taken into account:

a) Identify if the process is a bottleneck in production. If
the process is a bottleneck, then load shifting is
usually impossible without investment into the
process output increase.

b) Process cycle time (C/T) is slower or faster than its
next process C/T. C/T in VSM describes how often a
part or a product is completed by a process. If the
process C/T is slower than the process coming next,
it is possible to increase intermediate stock at the end
of the process coming after rather than at the end of
the first process. In case the first process is faster
than the next one, the LS applied will increase the
intermediate stock.

c) Weather process uptime (UPTIME) is high or low,
shows important information about process
reliability. A low reliability process has a negative
effect on LS.

d) If stock between processes is high or low, the reasons
should be found out before applying load scheduling.

e) Change over time (C/O) is long or short. C/O is the
time period for switching the process unit from one
product to another. It can show also time for cold
start of the process. C/O can highlight important
information about the process start up time.

The best way to start defining the energy consumption of a
process is to make the consumer list. Consumer list should be
based on processes described with VSM. The consumer list
may be available in the facility electrical department. In that
case, consumer list is usually based on the power cabinets and
it has to be made process by process. If the consumer list is not
available, it should be done from scratch. As the purpose is to
find an initial energy intensive process, the consumer list can
be composed without actual measurements. The consumer list
should contain the following information: process name; device

Fig. 2. VSM with energy consumption and storage overview.

Supplier Customer

Receiving twice a week Daily shiping

Process A one shift Process B one shift Process C one shift
Storage Storage Storage Storage

c/t= 2 min 10 c/t= 4 min 15 c/t= 7 min 30
5 days C/O 2 hr units C/O 3 hr units C/O 4 hr units

UPTIME 94% UPTIME 91% UPTIME 98%
Nom. P 30 kW Nom. P 50 kW Nom. P 2 kW

kWh/unit 2 20 kWh/unit 5 75 kWh/unit 0,3 9
kWh kWh kWh

5 days 40 minutes 105 minutes 1 day
2 minutes 4 minutes 7 minutes

II I I

label; rated power; cosᵩ; nominal current; nominal voltage and
consumption type that can be either continuous, intermittent or
stand by.

Upon completion of the consumer list, traditional VSM
should be elaborated and total nominal loads in the process
added. As a result, elaborated VSM will show which processes
have sufficiently high energy intensity to gain benefits from LS
and on the other hand, to estimate the particular potential to the
process scheduling. After completing the estimation in
conjunction with experts from production, the VSM should be
elaborated further. Based on the measurements, energy
intensity or the production unit is to be found. As the energy
usage was previously estimated, measurements can be done in
the production process where energy usage is estimated to be
high or VSM shows that LS can be implemented without
investing to process capacities or intermediate stocks.

As VSM describes product flow, the energy intensity should
also be given per product or production unit. The unit energy
consumption in the production process should be calculated
based on the actual measurement. Fig. 2 shows a theoretical
example of VSM together with data from the consumer list and
energy intensity in the process. Energy intensity is energy
consumption in each process to make one unit. As can be seen,
process A C/T takes 2 minutes, i.e., one unit is completed
during 2 minutes in the process. The total nominal power of the
process is 30 kW and 2 kWh is consumed to make one unit.
First intermediate stock capacity (after process A) is 10 pieces.
It consists of 20kWh energy which is available for scheduling.
Process B C/T is 4 minutes and the total nominal power in that
process is 50 kW. As the second intermediate stock (after
process B) capacity is 15 pieces, it has 75 kWh energy available
for scheduling. Process C cycle time is 7 minutes, total nominal
power is 2 kW and 0.3 kWh is consumed for making one unit.
We can see that process A C/T is 2 times faster than process B
and process C cycle time is 3.5 times faster than process A. As
a result, we can conclude that the last process will dictate the
whole process time and previous processes can be scheduled
taking into account the possibilities of the last process. For that
reason, we need to know how much time it takes to empty the
intermediate stock before the slowest process, which we call
buffer time (BT). In order to find BT, process C/T must be
multiplied with an available intermediate stock capacity (ASC)
(3) and (4).

SSCMSCASC −= . (3)

ASCTCBT ⋅= / . (4)

ASC is a difference between maximum stock capacity

(MSC) and safety stock capacity (SSC). SSC is to be defined
by production management.

BT shows the maximum load scheduling period in the
process. Neglecting safety stock capacity, in our example BT is
105 minutes. The total energy we can shift during 105 minutes
is 95 kWh, which is the sum of stored energy in two
intermediate stocks with 75 and 20 kWh accordingly.

IV. EXPERIMENTAL LOAD SCHEDULING ANALYSIS
IN A DISTRICT HEATING PLANT WITH IMPROVED

VSM METHODOLOGY
An example of the method above is described here. This

example covers a district heating plant in Paide, Estonia. The
company has one 8 MW woodchip boiler, several boilers
fuelled with shale-oil and one CHP plant based on woodchips
and with 8 MW thermal, 2 MW electrical output. Fig. 3 shows
the process of the plant. Woodchip boiler stock (moving floor)
is filled by a conveyor from the main storage (moving floor).
The main storage is filled by the incoming tucks or a wheel
loader. The company has 5-day storage available on site.
Woodchip boiler stock can contain woodchips a day with boiler
nominal load i.e. 180 m³. The conveyor between the main
storage and the woodchip boiler stock has a maximum output
of 50 m³/h. The conveyor from the woodchip boiler stock to the
boiler has a strict limitation for processing output from the
woodchip boiler. As it has no stock or daily silo available
between the boiler and the conveyor, the output is 7.5 m³/h with
the nominal boiler load. We start the cost reduction estimation
from LS by modeling the process using VSM as a basis. One
cubic meter of woodchips is taken here as one unit in VSM.

According to the conveyor output parameters, the total output
time is 6 days and 15.2 minutes and the value creating time is
17.2 minutes (time when one cubic meter of woodchips is
actually processed). Also, the VSM shows that the conveyor
from the main to the boiler stock has excessive capacity and is
able to process one cubic meter of woodchips approximately
6.67 times faster than the process bottleneck, i.e. woodchip
boiler.

The boiler will process one cubic meter of woodchips into
heat in 8 minutes. It can be concluded from VSM that there is a
possibility of LS of the conveyor from the main storage to the
boiler stock. It must be emphasized that we are dealing with a
biomass conveyor; therefore, the following simplifications are
used:

a) woodchip processing by a boiler is calculated based
on the nominal load, and boiler efficiency parameter
is 0.8;

b) woodchip energy intensity is approximated at 1.3
MW/m³, in real life it can be different, based on the
fuel type and humidity level.

The next task is to estimate process energy intensity by
making a consumer list behind the process. In our example, it is
evident from VSM that the only process that can be scheduled
is the process called “Conveyor from Main Stock to Boiler
Stock”. Thus, we need to make a consumer list behind this
process, which is given in Table I

Table I shows that the total nominal power behind the
process is 73.5 kW. The consumer list is necessary to estimate
of the process energy demand. It is possible that some
consumer’s nominal power is much greater than the actual
absorbed power. Therefore it is necessary to measure the
process energy use.

In our example, Fluke 1735 is used for measurements of the

process energy use and current measurements for some
continuous consumption. There is calculated absorbed power
with the following (5).

ϕcos3 ⋅⋅⋅= ff UIP , (5)

where P is absorbed power, If- phase current, Uf- phase

voltage and cosᵩ is power factor. By using absorbed power for
a continuous load, energy consumption was estimated as well.
Total energy consumption for processing 1 m³ of woodchips
was 0.96 kWh. Complete VSM for the process examined is

given in Fig. 3.
The VSM shows that by processing one cubic meter of

woodchips there is consumed 0.96 kWh of electrical energy. As
there is storage available with 180 m3 and SSC is 7.5 m3, ASC
is 172.5 m3 using (3). By knowing process C/T behind the
intermediate stock- it is 8 minutes per m3. There can be found
out that BT is 23 hours by using (4).

We can conclude that BT is sufficiently long for considering
LS from HPP to LPP given in section II. Paide boiler plant is
connected to a grid at low voltage line, so the grid tariffs
applicable will be different from those given in section II. HPPP
for the current example is 83.4 euros per MWh with 43.5 euros
per MWh grid tariff and LPPP is 55.2 euros per MWh with 25.7
euros per MWh grid tariff. Based on (1), we can calculate
potential saving which is 33.8%.

As stored energy in the process is 0.166 MWh, by
multiplying that with HPPP, the cost for the company will be
13.81 euros per day and saving from LS will be 4.67 euros per
day.

In the previous example, the VSM process is not
complicated; however, VSM is essential in case production is
more complicated and processes are more complex and
dependent on each other. In that case, VSM enables cost
savings with LS. Moreover, LS is well understandable with
VSM for staff involved in production management and
planning.

V. CONCLUSION
Various aspects of DSR were studied. Focus was on the price

difference in the electricity stock market in Estonia. If the loads
are shifted from HPP to LPP LS will enable a cost reduction of
31%. The methodology based on VSM was introduced where
cost savings were achieved with LS. Our analysis of
experimental load scheduling was based on the district heating
company in Estonia. The proposed methodology was found to

be applicable as a straightforward tool to achieve cost savings.
It can serve as first step for industry when implementing LS
because of its simplicity. With minor costs, reduced time and
lower complexity, a major cost saving can be achieved with
simple solutions such as the method proposed.

TABLE I
CONSUMER LIST FOR CONVEYOR FROM MAIN STOCK TO BOILER STOCK
Description Pn Cos ϕ In Un Consumption
Conv. Screen to
dist. Conv.

15 0.8 46.9 0.4 Continuous

Distr. conveyor 7.5 0.8 23.4 0.4 Continuous
Floor to
conveyor motor

4 0.8 8.6 0.4 Continuous

Floor to
conveyor motor

4 0.8 8.6 0.4 Continuous

Hydro pack 15 0.85 28.9 0.4 Intermittent
Hydro pack 15 0.85 28.9 0.4 Intermittent
Screen 5.5 0.81 11.4 0.4 Continuous
Conv. floor to
screen

7.5 0.8 23.4 0.4 Continuous

Fig. 3 VSM for LS for Paide district heating company..

Supplier Paide Boiler Plant Customer
receiving daily continuous heat supply

Storage Storage
c/t (min) 1.2 c/t (min) 8 c/t (min) 8

5 days C/O 0 1 day C/O 0 C/O 4 hr

Uptime 95% Uptime 95% Uptime 95%

600 (m³) Nom. P 73.5 (kW) 180 (m³)
kWh/m³ 0.96 173 (kWh) kWh/m³ 0.3

5 days 1 day
1.2 min 8 min 8 min

Conveyer from
Storage to Boiler

Stock
Conveyer from Boiler

Stock to Boiler Boiler

REFERENCES
[1] P. Kontogiorgos and G. P. Papavassilopoulos, "Subsidized power

interruption for reducing peaks in energy demand: A stackelberg game,"
Communications, Control and Signal Processing (ISCCSP), 2014 6th
International Symposium on, Athens, 2014, pp. 421-424.
doi: 10.1109/ISCCSP.2014.6877903

[2] G. Carpinelli, S. Khormali, F. Mottola and D. Proto, "Demand response
and energy storage systems: An industrial application for reducing
electricity costs. Part II: Numerical application," Power Electronics,
Electrical Drives, Automation and Motion (SPEEDAM), 2014
International Symposium on, Ischia, 2014, pp. 1038-1042.
doi: 10.1109/SPEEDAM.2014.6872040

[3] S. A. Raziei and H. Mohscnian-Had, "Optimal demand response capacity
of automatic lighting control," Innovative Smart Grid Technologies
(ISGT), 2013 IEEE PES, Washington, DC, 2013, pp. 1-6.
doi: 10.1109/ISGT.2013.6497854

[4] M. Houwing, R. R. Negenborn and B. De Schutter, "Demand Response
With Micro-CHP Systems," in Proceedings of the IEEE, vol. 99, no. 1,
pp. 200-213, Jan. 2011.
doi: 10.1109/JPROC.2010.2053831

[5] A. Rosin, I. Drovtar, S. Link, H. Hõimoja, H. Mõlder and T. Möller,
"Analysis of large electricity consumers demand profiles to determine
implementation of demand-side management measures”, “Tarbimise
juhtimine ‒ suurtarbijate koormusgraafikute salvestamine ning analüüs
juhtimise rakendamise võimaluste tuvastamiseks," Eleringi toimetised,
pp. 1-57, 20 October 2014.

[6] S. Mohagheghi and N. Raji, "Dynamic demand response solution for
industrial customers," Industry Applications Society Annual Meeting,
2013 IEEE, Lake Buena Vista, FL, 2013, pp. 1-9.
doi: 10.1109/IAS.2013.6682583

[7] A. Mohamed and M. T. Khan, "A review of electrical energy management
techniques:supply and consumer side (industries)," Journal of Energy in
Southern Africa, vol. 20, no. 3, 2009.

[8] A. Kuhi Thalfeldt and P. Raesaar, "Possibilities and feasibility of demand-
side management in the Estonian industrial sector," in 5 th International
Symposium "Topical Problems in the Field of Electrical and Power
Engineering”, Kuressaare, 2008.

[9] T. Ohno, “Toyota production system: beyond large-scale production”,
Portland, 1988.

[10] B. Haefnera, A. Kraemera, T. Staussa and G. Lanzaa, "Quality value
stream mapping," in 47th CIRP Conference on Manufacturing Systemss,
Windsor, 2014.

[11] P. Uuemaa, J. Valtin, J. Kilter, A. Puusepp, I. Drovtar and A. Rosin, "Load
control implementation in the energy intensive industry," Mediterranean
Electrotechnical Conference (MELECON), 2014 17th IEEE, Beirut,
2014, pp. 213-218.
doi: 10.1109/MELCON.2014.6820534

[12] I. Drovtar, P. Uuemaa, A. Rosin, J. Kilter and J. Valtin, "Using demand
side management in energy-intensive industries for providing balancing
power - The Estonian case study," Power and Energy Society General
Meeting (PES), 2013 IEEE, Vancouver, BC, 2013, pp. 1-5.
doi: 10.1109/PESMG.2013.6672418

[13] P. Uuemaa, J. Kilter, J. Valtin, I. Drovtar, A. Rosin and A. Puusepp,
"Cost-effective optimization of load shifting in the industry by using
intermediate storages," Innovative Smart Grid Technologies Europe
(ISGT EUROPE), 2013 4th IEEE/PES, Lyngby, 2013, pp. 1-5.
doi: 10.1109/ISGTEurope.2013.6695404

[14] AS, Nord Pool Spot, "Elspot_prices_2014_hourly_EUR," 2014. [Online].
Available: http://www.nordpoolspot.com/historical-market-data.
[Accessed 20 June 2015].

[15] Elektrilevi OÜ, "Price list fo Elektrilevi from 01.04.2014”, “Elektrilevi
hinnakiri alates 01.04.2014," 1th April 2014. [Online]. Available:
www.elektrlilevi.ee/hinnakiri. [Accessed 19th June 2015]

I. INTRODUCTION
a) conservation - energy saving is achieved through static methods;
b) load growth - energy consumption is increased when an energy system has surplus energy production;
c) valley filling - load is increased through the off-peak periods or keeping stable consumption;
d) peak clipping - energy consumption is decreased in peak periods;
e) load shifting - peak consumption is shifted from peak periods to non-peak periods;
f) seasonal load reduction - annual energy peaks are reduced.

II. II. ENERGY PRICE AS A DRIVER FOR LOAD SHIFTING
III. IMPROVEMENT OF VALUE STREAM MAPPING METHODOLOGY FOR EVALUATION OF LOAD SCHEDULING POSSIBILITIES
A. Load Scheduling at Intermediate Storage Use in Production
B. Methodology for LS with VSM.
a) Identify if the process is a bottleneck in production. If the process is a bottleneck, then load shifting is usually impossible without investment into the process output increase.
b) Process cycle time (C/T) is slower or faster than its next process C/T. C/T in VSM describes how often a part or a product is completed by a process. If the process C/T is slower than the process coming next, it is possible to increase intermediate...
c) Weather process uptime (UPTIME) is high or low, shows important information about process reliability. A low reliability process has a negative effect on LS.
d) If stock between processes is high or low, the reasons should be found out before applying load scheduling.
e) Change over time (C/O) is long or short. C/O is the time period for switching the process unit from one product to another. It can show also time for cold start of the process. C/O can highlight important information about the process start up time.

IV. EXPERIMENTAL LOAD SCHEDULING ANALYSIS IN A DISTRICT HEATING PLANT WITH IMPROVED VSM METHODOLOGY
a) woodchip processing by a boiler is calculated based on the nominal load, and boiler efficiency parameter is 0.8;
b) woodchip energy intensity is approximated at 1.3 MW/m³, in real life it can be different, based on the fuel type and humidity level.

V. Conclusion
References