Energy Efficiency (2014) 7:271–284 DOI 10.1007/s12053-013-9223-9
ORIGINAL ARTICLE
Integrating renewables economic dispatch with demand side management in micro-grids: a genetic algorithm-based approach
Ahmer Arif · Fahad Javed · Naveed Arshad
Received: 14 February 2012 / Accepted: 6 August 2013 / Published online: 8 September 2013 © Springer Science+Business Media Dordrecht 2013
Abstract Economic dispatch and demand side Keywords Demand side management · management are two of the most important tools Economic dispatch · Smart grids · Micro-grids · for efficient energy management in the grid. It Intelligent energy management is a casual observation that both these processes are intertwined and thus complement each other. Nomenclature Strategies aiming to optimize economic dispatch have implications for demand side management VC Cut-in speed of wind generator techniques and vice versa. In this paper, we VR Rated speed of wind generator present a genetic algorithm-based solution which VF Cutoff speed of wind generator combines economic dispatch and demand side C The set of consumers under the micro-grid management for residential loads in a micro-grid. S The set of currently available energy Our system collects preferences of demand data suppliers to the micro-grid from consumers and costs of energy of various μx Total energy demand by consumer x sources. It then finds the optimal demand schedul- μx,h Total energy demanded by consumer x at ing and energy generation mix for the given time hour h window. Our evaluations show that the given ap- αy,h Total energy available from supplier y at proach can effectively reduce operating costs in a hour h single- and multiple-facility micro-grids for both λy,h Price of energy from supplier y at hour h suppliers and consumers alike. βx,y,h Total energy allocated to consumer x from supplier y at hour h
A. Arif · F. Javed · N. Arshad (B) Department of Computer Science, Introduction School of Science and Engineering, Lahore University of Management Sciences, DHA, Sector U, Lahore, Pakistan Economic dispatch and demand side management e-mail: [email protected] (DSM) are two of the most important tools for A. Arif efficient energy management in the grid. Whereas e-mail: [email protected] economic dispatch is the task of finding the right F. Javed and optimal mix of energy for the given demand, e-mail: [email protected] demand side management aims at managing end 272 Energy Efficiency (2014) 7:271–284 user demand to reduce cost. There are various devices and their average generation and con- ways to achieve this goal, the most common way sumption similar to the works of Shivakumar et al. is by shifting movable loads to off-peak time or (2013), Livengood and Larson (2009), Ranade and times of lower cost. The goal is to reduce en- Beal (2010), and Conejo et al. (2010). Study of ergy cost by balancing energy resources against integration of power flows in the micro-grid and demand. integration of different energy sources for a robust It is a casual observation that both these power delivery is the next step of deploying this processes are interlinked. Economic dispatch tries system but is beyond the scope of the current to reduce cost according to available demand and work. DSM tries to manage demand to reduce cost. It The remaining sections of the paper are orga- stands to reason that if both tasks are consid- nized as follows: In “Economic dispatch and DSM ered together, where DSM and economic dispatch opportunities in smart grids,” we discuss the op- planning include their interdependent constraints, portunities for economic dispatch and DSM that a better solution may emerge. have emerged through research in smart grids Specifically, in renewable solution imple- and micro-grids. In “Operational system,” the mented on a small scale by the consumers, this operation system is discussed and in “Problem aspect becomes more evident. In these systems, formulation,” the problem present in this opera- typically, the excess low-cost energy from renew- tional system is modeled and design objectives able sources is either stored in battery systems or are discussed. In “Genetic algorithm,” the ge- sold back to energy retailers up the grid. These ap- netic algorithm formulation and its algorithm is proaches miss the opportunity to take advantage discussed. “Model implementation” specifies im- of the localized aspects of micro-grids and incur plementation details for the various steps for the significant costs associated with often expensive, genetic algorithm, and finally, in “Evaluations and inefficient energy storage systems and transmis- results,” evaluation of the numerical results and a sion losses upstream to the grid. By more immedi- summary of the findings are presented. ately utilizing this excess energy, these losses are minimized and equal or lower energy costs can be achieved with potentially fewer distributed re- Economic dispatch and DSM opportunities newable resources, creating savings in the installa- in smart grids tion, maintenance, and operating costs associated with them. There are two developments in recent times which In this paper, we present a strategy to inte- have provided new opportunities for economic grate DSM and economic dispatch for renewables dispatch and DSM. On one hand, smart grid ini- in a localized setting. In our strategy, user pref- tiatives have provided greater surgical control of erences and generation capacity are encoded as user-side power consumption (Coll-Mayor et al. constraints of the system. With these constraints, 2007; Farhangi 2010; Ipakchi and Albuyeh 2009). an optimization objective function is constructed This is being used as an opportunity by various from the cost of generation for each unit for each researchers to define fine-grained DSM (Conejo hour. Afterwards, a genetic algorithm is used to et al. 2010; Gudi et al. 2011; Livengood and find the optimal solution for the problem. Our Larson 2009; Ranade and Beal 2010) as opposed results show that this integrated methodology to the blanket DSM employed in traditional grids is up to 15 % more cost-effective than state- (Amjady 2007; Gellings and Chamberlin 1993; of-the-art independent DSM and economic dis- Shahidehpour et al. 2002;Weron2006). On the patch models for a neighborhood as compared to other hand, distributed generation and renewable our combined DSM and economic dispatch im- sources have enabled the emergence of micro- plementation for an integrated neighborhood grids within which more efficient and exact eco- approach (Gudi et al. 2011). nomic dispatches are possible (Jiayi et al. 2008; Our study is focused on the abstraction of Lasseter et al. 2002; Tsikalakis and Hatziargyriou houses, renewable energy resources (RES), and 2008; Yalcinoz et al. 2001). Energy Efficiency (2014) 7:271–284 273
Demand-side management programs in tradi- economic dispatch in micro-grids (Lasseter et al. tional grids usually involved manual control of 2002). A genetic algorithm-based approach by end user devices (Gellings and Chamberlin 1993). Yalcinoz and colleagues can also be found in the Automation is proposed by some authors, but the current literature (Yalcinoz et al. 2001). However, control was blanket in the sense that the par- all of these systems cater to economic dispatch ticipating devices were either switched on or off independent of DSM. (Albadi and El-Saadany 2007). There is no con- Recently, a series of researchers have de- cept of intelligent planning for the operation of veloped economic dispatch solution incorporat- these devices. ing the demand response saving. For instance, Fine-grained DSM strategies, on the other Behrangrad et al. presented an economic dispatch hand, can perform complicated automated device model which uses the demand response as spin- scheduling based on consumer preferences and ning reserve (Behrangrad et al. 2011). Parvania electricity prices. Furthermore, with the advent and Firuzabad presented an economic dispatch of more dynamic forms of pricing, they have be- model which used the demand response planners come increasingly flexible in order to adjust for (DRPs) as an integral part of grid and mod- fluctuations in the cost of electricity. These DSM eled the saving realized by DRPs in planning techniques usually manage energy within a sin- optimal economic dispatch (Parvania and Fotuhi- gle household and typically involve capitalizing Firuzabad 2010). However, in both the instances, on variable pricing by energy providers and in- economic dispatch does not actually plan house- tegration of house-based renewables for cheaper hold consumption in the same way as Conejo et al. energy utilization. Several papers have already (2010), Livengood and Larson (2009), or Ranade been published which relate to such techniques. and Beal (2010) do. An example is the work presented by Livengood As demonstrated later in our experiments, and Larson (2009) which uses a stochastic pro- treating DSM and economic dispatch as indepen- gramming solution to optimize user-side demand. dent modules can result in suboptimal power pric- However, the approach focuses on optimizing ing across the grid. Instead, in this paper, we com- for a single household at the granularity of in- bine fine-grained DSM with economic dispatch of dividual consumer devices. On the other end of micro-grids and attempt to find an optimal sched- the spectrum is the probabilistic demand shaping ule for both under a single optimization model. and peak-shaving approach outlined by Ranade This way we are able to leverage the power of and Beal (2010) which concerns itself with reduc- DSM to arrive at better power dispatches, result- ing demand by multiple consumers during peak ing in a lower cost than independent DSM and hours but does not focus on actual scheduling of economic dispatch systems. activities. We specifically consider the micro-grid devel- Economic dispatch in the scope of micro-grids oped within the EU R&D MG project and pre- has also generated a fair amount of interest. The sented by Jiayi et al. in their survey (Jiayi et al. main contention here has been to incorporate 2008). In this micro-grid is a hierarchical structure renewables (Jiayi et al. 2008). While Celli and where a micro-grid controller (MGCC) acts as a colleagues have proposed methods to use micro- broker between load controllers (LC) and micro- grid economic dispatches to maximize benefit in sources (MS). In the survey, it has been shown integration with the rest of the grid, Tsikalakis that LC and MS are two independent entities suggests a system where intelligent controllers and optimize their operations independently. We at consumer level collaborate with a micro-grid show that a combined model at MGCC level, at central controller (Tsikalakis and Hatziargyriou least for the renewable MS, provides a better op- 2008). In this case, the central controller is respon- timization than independent LC and MS planning. sible for dispatch whereas local controllers are re- This is under the assumption that micro-grid sponsible for DSM (Tsikalakis and Hatziargyriou implements an economic operational cycle similar 2008). In addition to these solutions, Lasseter and to that in the work of Dimeas and Hatziargyriou colleagues have also designed a framework for (2005). In this setup, MGCC announces the 274 Energy Efficiency (2014) 7:271–284 beginning of market period and MS and LC pre- available to mitigate the response time of MS and dict their production and demand, respectively, intermittency of RES. However, unlike other DR and bid for the resources under MGCC. MGCC systems (Gudi et al. 2011; Livengood and Larson then resolves the market using some algorithm. 2009), we do not consider this storage as an active When we consider DSM within this setup, we source in our planning. The storage is placed as see a point of combined optimization for MS a backup measure to safeguard against failures. and LC. Using this storage for any purpose can place our To our knowledge, such solutions for micro- system in a compromised state which we would grids are rare. Approaches that attempt to bring like to avoid under any circumstance. Further- together both sides of this relationship are limited more, for our optimization, we only consider the in their scope. For example, the linear program- hourly average wind speed and solar irradiance. ming optimization solution utilized by Conejo This, in conjunction with the storage, provides us and colleagues is only applicable to a single-load, with sufficient buffer to plan DR. This treatment single-supplier scenario (Conejo et al. 2010). Sim- of renewable is consistent with similar works in ilarly, the particle swarm optimization of Wang the literature (Gudi et al. 2011; Livengood and and colleagues handles multiple sources but is Larson 2009). designed to function on the scale of single-facility To test the feasibility of our approach versus micro-grids only (Gudi et al. 2011). A need exists existing solutions, we have benchmarked it on two for a more holistic approach that can encom- levels. At the first level, we construct a power pass both these aspects of energy management dispatch and DSM system for a single house and while accommodating multiplicity of suppliers and compare it with a state-of-the-art energy man- consumers. agement system (Gudi et al. 2011) at the same In this paper, we satisfy this need by present- scale. Our results show that our genetic algorithm- ing a genetic algorithm-based solution for com- based solution is up to 10 % more effective. We bined power dispatch and DSM for residential then extrapolate to multiple houses that would users in a micro-grid. Our proposed strategy has a be present in a micro-grid and show that our bottom–up approach where individual consumers technique is able to utilize the combination of in the micro-grid communicate their power re- DSM and economic dispatch to deliver higher quirements as a set of constraints over a neigh- savings than DSM and economic dispatch systems borhood area network to the local micro-grid. working independently. In this way, our contri- The energy management system (EMS) of the bution is unique that it brings the best of these micro-grid then collates this information and uses two strategies together and forms a succinct so- it in conjunction with information from available lution for energy management at the level of a power supplies to generate an optimum power micro-grid. distribution schedule. This schedule satisfies the maximum number of consumer constraints at the lowest possible total cost to all the consumers. Operational system It is important to note that while this algorithm can be applied on an individual consumer level, In this section, we discuss the operational sys- the primary focus is to optimize across multi- tem for which our technique is designed. Our ple small-scale consumers located within a single proposed strategy is for a micro-grid setup with micro-grid. variety of micro-generation and renewable energy An important question within the scope of re- resources. Houses have some home area network newable integration is their intermittency. It has or smart devices setup which can monitor and already been established that integration of MS control energy consumption. in micro-grid and for RES requires an interme- The planning module operates on a 24-h plan- diate storage. However, the sizing of battery is ning horizon for its scheduling. It is assumed that problematic and adds cost to the system. In this pricing and availability details of energy supplies paper, we assume that a storage of sufficient size is are known 24 h in advance due to day-ahead pric- Energy Efficiency (2014) 7:271–284 275 ing. It is also assumed that consumption schedules generation unit, the energy dispatch from that for the consumers are available. These schedules unit should be less than or equal to its projected are in the form of demand of energy in each hour generation. Each of these
(chromosomes), which encode possible solutions Model implementation (phenotypes) to an optimization problem, evolves toward better solutions. The evolution starts from In this section, we will discuss the details of each a population of randomly generated chromo- step of our genetic algorithm implementation. We somes and happens across a prespecified number will discuss steps 1 to 6 since steps 7 and 8 are of generations. For every generation, the fitness trivial. of all chromosomes in the population is calculated using the objective function. Multiple chromo- Input representation somes are stochastically selected from the current population (based on fitness to some degree) to Generation shift factors are specified as simple form the gene-pool for the next population. The real number values. Supplier information is mod- next population is populated using a combination eled as a 3-tuple which holds the supplier iden- of the very best solutions from the previous gen- tifier, energy retail price, and a list of how much eration (elitism) and modified chromosomes from kilowatts of energy is available for each hour of the gene pool (cross-over and possibly randomly the 24-h window in question. Consumer informa- mutated). The new population is then used in tion is modeled as a simple 2-tuple that contains the next iteration of the algorithm. In this way, the consumer identifier and total energy required. fitter solutions have a higher probability of being Similarly, consumer constraints are modeled as a reproduced in the next generation and mutation 5-tuple holding a consumer identifier representing and crossover help avoid falling into the trap of whom the constraint applies to, the amount of local minima of results by ensuring that the chro- energy to be delivered, and the range of hours mosomes do not become too similar. it must be delivered in. The fifth field denotes The computational procedure used in the ap- the basic quantity of energy the algorithm should proach is laid out as follows: allocate when handling the constraint. It makes no sense to allocate 9 kWh in 1 h and 1 kWh in Algorithm 1 DSM–economic dispatch planner the next if the constraint was built to model the Step 1: Input supplier, consumer, and consumer functioning of a pair of appliances that required constraint tuples and specify genera- 5 kWh each and could be scheduled in separate tion shift factors: population size, num- hours or together. Specifying 5 in this field would ber of generations, mutation probability, ensure that only useful allocations are made. elitism percentage, and tournament size. Step 2: Pseudo-randomly generate the initial Phenotype representation population. Step 3: Rank chromosomes using the fitness A fundamental aspect of genetic algorithms function described in Weron (2006). (GAs) is the encoding of solutions appearing in Step 4: Apply elitism by reserving slots in the the population. This encoding, along with the as- next generation for the highest scoring sociated decoding to return to the natural problem chromosomes of the current generation. space, is essential to the GA operations. Each Step 5: Build gene pool for the next generation phenotype consists of a set of elements called by the Tournament Selection scheme. genes that are 4-tuples holding the consumer iden- Step 6: Apply Crossover and Mutation opera- tifier, the supplier identifier, the energy allocated, tors on gene pool members to repopulate and the hour in question (1–24). When encoded the next generation. as a chromosome, each of these fields is repre- Step 7: Repeat steps 3–6 until some terminating sented as a real number. While binary encoding is generation number is reached or time traditionally the most common method, we have limit is exceeded. utilized the real-valued representation scheme Step 8: Output the fittest chromosome as the for the benefits it offers in numerical function proposed optimum solution. optimization (Gomez-Villalva and Ramos 2003; Energy Efficiency (2014) 7:271–284 277
Philpott and Pettersen 2006). Using real-number energy required. However, if the basic encoding increases the efficiency of the GA since quantity exceeds the total energy re- conversion of the chromosomes to binary is no quired, randomly pick a value from 1 up longer needed, avoids loss in precision due to to the total energy required. discretization to binary, requires less memory as Step 5: Consider the selected hour, supplier, and it relies on the computer’s internal floating-point power to allocate—along with the con- architecture, and offers the choice to use a greater sumer identifier from the constraint—as variety of genetic operators (Borenstein 2005). a gene and append it to the chromosome. Step 6: If the total energy required by the con- Generating the initial population straint has not been satisfied, update this quantity by subtracting the power allo- One open problem faced by evolutionary algo- cated in step 4 and repeat from step 2. rithms that handle constraints for numerical opti- Step 7: Pick the next constraint and repeat from mization problems such as this one is that of build- step 2 until all constraints have been ing an initial population of chromosomes that is satisfied. actually feasible at all. When the fitness function is complex and noncontinuous in combination with a search space that is relatively large, GAs can This ensures that any generated solution sat- struggle. This is the case here where a solution isfies Eqs. 2 and 4. Additional energy required must always fulfill Eqs. 2 and 3 to be valid at all by the consumer that was not covered by any and must fulfill Eq. 4 in order to maintain the constraint is then handled by generating genes re- quality of service threshold at acceptable levels peatedly through a completely random procedure for consumers. The cardinality of these consumer- until all requirements have been met. Solutions specified constraints can reach over 200 for a rela- generated in this fashion can still violate Eq. 3 tively large micro-grid. Every consumer has fixed since overallocation on a particular supplier can but different minimum consumption for every occur so checks should be evaluating the fitness of hour of the day along with variable energy re- a solution. quirements that lie within specific time intervals. Generating even a single solution randomly that Elitism is capable of satisfying each of these constraints can be time-prohibitive. A fraction of the fittest chromosomes Ne are guar- To overcome this challenge, our approach uses anteed a place in the next generation directly with- a pseudo-random technique that ensures user- out any mutation or crossover operators applied. specified constraints are satisfied while generating This helps ensure that if an optimum chromosome an initial population of size N: is found, it remains a competitive candidate. Note Algorithm 2 Population generation that these elitist chromosomes in the original pop- Step 1: Select the first constraint from the list of ulation are also eligible for selection and subse- user-specified constraints. quent recombination in the gene pool. Step 2: Randomly select an hour from within the time range given in the constraint. Selection scheme Step 3: Pick a supplier by filtering for the set of suppliers that are selling energy at After elitism has been applied, a subset of the that hour and randomly selecting one of N chromosomes is selected to be used as parents them. for the succeeding generation. It is essential that Step 4: Determine how much power to allocate priority is not monopolized by chromosomes with by randomly picking a multiple of the the highest fitness values since this tends to re- basic quantity of energy defined in the duce the diversity, often causing premature con- constraint (See section A) up to the total vergence in a local optimum. To avoid this, our 278 Energy Efficiency (2014) 7:271–284 approach uses tournament selection. Chromo- according to an interchange of parts of the somes are inducted into (N − Ne) “tournaments” chromosome structure of the two parents. We by random sampling (with replacement) from the accomplish this by one-point crossover, where original population. The winner of each of these the numbers appearing after a randomly cho- tournaments (the one with the best fitness) is sen dividing (splice) point in the genes of the placed in the gene pool as a parent for the next two chromosomes are interchanged. generation of chromosomes. Stronger chromo- 2. Mutation operator: For a chromosome sub- somes can enter the pool multiple times, but by ject to mutation, we take a randomly chosen keeping the tournament size small, weaker chro- subset of its genes. For each of these genes, mosomes have a reasonable chance to be selected mutation is accomplished by adding a small in- as well (Holland and Mansur 2006). dependent normal to the hour at which power was to be supplied and switching the supplier Reproduction scheme identifier to another randomly selected valid value.
The (N − Ne) parent chromosome entries from the gene pool are paired together to help breed the (N − Ne) new chromosomes needed to repop- ulate the next generation. Pairs are subjected to a Evaluations and results crossover operator with a prespecified probability which produces two offspring. If the crossover Experimental setup operator is not applied, the offspring will be di- rect clones of the parents. Since a given popula- In this section, we describe the set of experiments tion might not contain enough diversity to find designed to evaluate the feasibility of our ap- the solution via crossover operations alone, the proach. Figure 1 shows the flow of data in the sys- algorithm also uses a mutation operator on any tem. There are two input providers to the system. given offspring with a prespecified probability in The suppliers provide their day-ahead approxi- an attempt to generate novel solutions. Details on mate availability and costs. The consumer’s smart these two operators are given below: home system provides the planner with consump- tion constraints according to user’s preferences. 1. Crossover operator: If crossover does take The GA optimizer keeping the constraints and place, then the two offspring are produced cost in view creates a consumption schedule for
Consumption Supply schedule schedule Consumers Suppliers
Consumption Day-ahead requirements / availability constraints & costs GA Optimizer
Fig. 1 Flow of data in economic dispatch–DSM system. genetic algorithm will in return provide a plan to house- The suppliers will provide their day-ahead approximate holds to consume energy and will provide plans to genera- availability and costs. The consumer smart home system tors for optimal dispatch will provide the system with consumption constraints. The Energy Efficiency (2014) 7:271–284 279
Fig. 2 Unit commit for renewables: Solid line with triangle markers represents the daily cost of power when X sets or renewables are installed in combined configuration. Dashed line represents the cost of 10 renewables in distributed controller configuration
the consumers and a dispatch schedule for the The wind speed and solar radiation data generating units (Fig. 2). utilized during evaluations are delineated in The GA optimization engine is set up with Tables 2 and 3. This information was obtained the following parameters: We set up our genetic from Lasseter and Paigi (2004) and Zareipour algorithm with a population size of 1,000 across et al. (2004), respectively, and mirrors the values 100 generations with a tournament size of 3, used in the case study outlined by Gudi et al. and elitism, mutation, and crossover probabilities (2011). All the experiments were run on a com- were set at 0.02, 0.05, and 0.5, respectively. The modity computer with core i5 processor and 8 GB choice of these parameters was guided by Gomez- of memory. Villalva and Ramos (2003). The daily cost rates for consumption of electric- ity used in both experiments are part of a cost rate Experiment 1: single-house distributed structure plan obtained from Gudi et al. (2011) configuration benchmarking andshowninTable1. For renewable sources of energy, we utilize To evaluate the feasibility of our approach ver- the wind turbines and photovoltaic cell. The sus existing solutions, we benchmarked our GA specifications for these sources were also acquired against the state-of-the-art energy management from Gudi et al. (2011) and set as follows: solution presented in the study by Gudi et al. (2011). Photovoltaic cell: Area 20 m2 The case study therein incorporates distributed Efficiency 12.5 % renewable resources and utilizes detailed con- Wind turbine: Rotor diameter 4.75 m sumption information of sample household appli- VC 2 m/s ances to output a consumption schedule that min- VR 8 m/s imizes total cost to the micro-grid. The properties VF 25 m/s (number of appliances, time of operation, power Turbine efficiency 35 % consumption, and priority) of the appliances were setasshowninTable4. It should be noted that ap- pliances with multiple-duty-cycle operation have Table 1 Energy rates for experiment (Gudi et al. 2011) variation in their peak power consumption. The Time Cost (cents/kWh) goal of the DSM is to reduce the cost of energy 7 a.m. to 9 a.m. 0.45 and is constrained by the practical consideration 9 a.m. to 4 p.m. 0.30 that all the operations for the devices must be 4 p.m. to 6 p.m. 0.37 completed within the planning window. The se- 6 p.m. to 10 p.m. 0.45 lection of device is according to the priorities 10 p.m. to 7 a.m. 0.30 set by the user. This is the experimental setup 280 Energy Efficiency (2014) 7:271–284
Table 2 Solar insolation Hour of day 1 2 3 4 5 6 7 8 values Insolation (W/m2) 0 0 0 0 0 0 56 214 Production (W) 0 0 0 0 0 0 1.4 5 Hour of day 9 10 11 12 13 14 15 16 Insolation (W/m2) 346 703 955 1,044 989 949 899 789 Production (W) 9 18 23 26 25 27 22 20 Hourofday1718192021222324 Insolation (W/m2) 581 352 73 0 0 0 0 0 Production (W) 15 8 2 0 0 0 0 0 of Gudi and colleagues and is consistent with energy production from on-site renewable sources the experimental setup designed by Livengood across multiple consumer loads in the micro-grid and Larson (2009) and Ranade and Beal (2010). as will be shown in experiment 2. In this way, We see possible improvements in this scheme; we maximize the use of these resources during however, we would first like to present compar- off-peak hours and create savings by overcoming ison with the existing work to benchmark our energy losses to storage inefficiencies and mini- results. mizing the installation and operational costs asso- Based on the cost rate plan defined in Table 1, ciated with batteries and energy transformations. the results for optimal DSM planning using Gudi et al. (2011) and our genetic algorithm imple- mentation are shown in Table 5. There are three Experiment 2: distributed controller results for comparison. First is the base system configuration versus combined controller without DSM, and the second is when DSM is configuration applied but without any renewable resources. The third result is the savings of application of DSM Having established the feasibility of our algorithm using renewable sources. The percent cost savings with respect to alternate solutions at a single- for direct operation with renewable resource inte- consumer level, we extended the scale to simulate gration is 10.39 %. a multiple-facility micro-grid. For this, we used energy consumption data collected from 10 houses (Table 6). Due to privacy requirements, we added Results a small amount of random noise in the data while making sure that the characteristics of the data are The results for operation with renewable re- not affected. Our comparison is between a DSM sources are competitive but not immediately com- in a distributed controller configuration where parable directly since our results do not account each house has its own DSM planner against a for the use of a 100 % efficient energy storage DSM where loads of each house are combined system that begins with 40 % state of charge at the into a single system. This single system acts as onset of simulations in the study by Vittal (2010). the planner combining the energy produced and We opt out of using such energy storage systems the devices available for scheduling. The goal is to in active DSM planning in favor of sharing excess maximize the renewable energy usage across the
Table 3 Hourly average Hourofday1 2345678 wind speeds Wind speed (kmph) 45.2 16.8 5.9 34.4 20.7 27.2 36.8 12.2 Production (W) 0 831.25 325.53 0 831.25 0 0 831.25 Hour of day 9 10 11 12 13 14 15 16 Wind speed (kmph) 6.5 37.6 3 31.6 19.7 44.2 11.3 11.4 Production (W) 439.74 0 31.33 0 831.25 0 831.25 831.25 Hourofday1718192021222324 Wind speed (kmph) 13.7 27.6 34.9 20.9 31.1 3.24 35.1 2 Production (W) 831.25 0 0 831.25 0 42.90 0 0 Energy Efficiency (2014) 7:271–284 281
Table 4 Usage Appliance name Time of Time of operation No. of Consumption Priority consumption pattern of operation without DSM appliances (W) the user 3,500 Air conditioner Full day Full day 3 2,500 2 1,500 Clothes dryer 120 min 9 p.m.–11 p.m. 1 650 6 Dishwasher 180 min 9 a.m.–10 a.m. 1,200 4 9 p.m.–11 p.m. 1 1,200 800 Refrigerator Full day Full day 2 600 1 450 Pool pump 240 min 4 p.m.–8 p.m. 1 2,000 7 Washing machine 60 min 8 p.m.–9 p.m. 1 800 5 6 a.m.–11 a.m. 700 Water heater Full day* 11 a.m.–6 p.m. 1 500 3 6 p.m.–1 a.m. 700
micro-grid and avoid storage or inefficient back selling. Table 5 Experimental results: without DSM is the base To conduct this experiment, we executed our system running without any renewables and without apply- algorithm for each of the houses individually and ing any DSM added the values to obtain the total cost for the Scenario Cost (cents/day) neighborhood. Table 7 lists the cost of power in Benchmark Experiment cents per day for each house while each house uses Without DSM 131 – the DSM strategy of experiment 1. Each house With DSM 88.7 80.35 possesses its own wind turbine and photovoltaic With DSM & 22.5–26.9 23.47 renewable sources cell. The specifications for these were the same as those used in experiment 1. We use this result as With DSM is a system with a DSM but no renewable. With DSM and renewable is when houses are fitted with the benchmark and compare it against our com- renewable sources of energy, a DSM is applied to manage bined configuration model. the energy loads In the combined model, as compared to the pre- vious one, the houses could share excess energy from their renewable resources with each other. In this way, we leverage the combined energy pro- duction and larger device management domain to
Table 6 Independent run DSM results House no. Cost (cents/day) Table 7 Combined DSM results 1 20.49966 Renewable sets Cost (cents/day) 2 34.80037 1 519.9168 3 10.4551 2 438.0797 4 20.64298 3 358.3161 5 21.05456 4 278.9873 6 13.89449 5 254.5108 7 39.52733 6 245.005 8 25.22495 7 237.1987 9 29.82267 8 227.0373 10 12.23465 9 224.8754 Total 228.15676 10 217.5591 282 Energy Efficiency (2014) 7:271–284
Fig. 3 Total demand for 10 houses and availability of renewable over the 24-h period
increase system efficiency. As an illustration of the 5 % of operational expenses through a combined outcome, Fig. 3 shows the total demand for 10 configuration controller. houses and the generation through wind and solar panels. Conclusion and future work Results This paper provides a genetic algorithm to be Our first result compares the total energy cost for integrated into the EMS of a multi-facility micro- both configurations. Whereas our total cost with grid. The algorithm offers a scalable means of distributed controllers was 228.16 cents per day, minimizing energy costs to the micro-grid’s con- the cost using combined configuration came out sumers while meeting their time-based require- to 217.6. This is a 10.56 cents or 5 % of savings ments. Simultaneously, it assists energy suppliers by using exactly the same devices but a different by determining the optimum configuration of controller methodology. generational facilities that must be deployed to meet the stipulated demand. The interaction takes Discussion on unit commit problem place on a daily basis where energy consumption, user-specified constraints, and supplier day-ahead As a side note, we further investigated if we can pricing information is. A pair of case studies solve the unit commit problem with this setup. We demonstrates the feasibility and effectiveness of started with no renewables and calculated a plan the proposed algorithm to minimize total cost to by adding one set of renewables in each iteration. both the suppliers and consumers within a micro- Figure 2 shows a graph of this gradual increase of grid that integrates the proposed procedure in renewables in combined configuration against the its EMS. total cost of distributed controller configuration. There are many areas in which the algorithm We consider 10 sets of renewables in distributed might be further improved. Constraint-capturing controller configuration since combining power mechanisms are one obvious place: where inte- of more than one renewable set is not possible gration with negotiation algorithms that bridge in distributed configuration. We see that for the the gap between algorithms that operate on the same demand pattern, seven renewable pairs will individual consumer level such as those of the be sufficient to provide energy at the same energy study by Livengood and Larson (2009)and cost as compared to each house having its own the larger multi-facility micro-grid level would renewables and controllers. This means that we make the overall system more dynamic in the can save as much at 30 % of setup cost or save face of changing consumer behavior. Similarly, Energy Efficiency (2014) 7:271–284 283 ensuring fairness to individual consumers could form incorporating optimal management of distrib- be handled in a more sophisticated process using uted renewable resources. 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