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A Knowledge-based Approach to the Design of Integrated Renewable Energy Systems- Ramakur - Artigo

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  648 IEEE Transactions on Energy Conversion, Vol. 7, No. 4 December 1992. A KNOWLEDGE-BASED APPROACH TO THE DESIGN OF INTEGRATED RENEWABLE ENERGY SYSTEMS R. Ramakumar I. Abouzahr Senior Member, IEEE Member, IEEE Director Research Associate Engineering Energy Laboratory Oklahoma State University Stillwater, OK USA 74078 Keywords: Integrated Renewable Energy Systems (IRES) Design; Knowledge-Based Design Approach; Stand-Alone Energy System Design; Design of Renewable Energy Systems; Hybrid Systems; Wind-Electric Conversion Systems; Photovoltaic Systems; Biogas Usage. ABSTRACT Integrated Renewable Energy Systems (IRES) utilize two or more renewable energy resources and end-use technologies to supply a variety of energy needs, often in a stand-alone mode. A knowledge-based design approach that minimizes the total capital cost at a pre-selected reliability level is presented. The reliability level is quantified by the loss of power supply probability (LPSP). The procedure includes some resource-need matching based on economics, the quality of energy needed, and the characteristics of the resource. A detailed example is presented and discussed to illustrate the usefulness of the design approach. INTRODUCTION Global environmental concerns coupled with steady progress in renewable energy technologies are opening up new opportunities to hamess and utilize different manifestations of solar energy [1-3]. Nowhere are these opportunities more pertinent than in powering remote loads requiring a mixture of different grades of energy 14-61. Integrated Renewable energy systems (IRES) utilize two or more renewable energy resources and end-use technologies to supply a variety of energy needs [7,8]. Such systems are typically operated in a stand-alone mode. However, they can also function as easily in conjunction with conventional energy systems such as diesel-electric generators and/or utility grid connection. The most effective use for IRES s believed to be in the stand-alone mode to energize the two million or so remote villages in the world with no grid connection [9-111. Obviously, there are many other situations where IRES can contribute by providing energy without increasing the associated environmental burdens. The renewable energy resources that require consideration are (1) biomass, (2) insolation, (3) wind, and (4) hydro. All the energy needs can be consolidated into four categories as given below: 1. medium-grade thermal energy (100' C to 300' C), 2. low-grade thermal energy (less than 100' C), 3. rotating shaft power (fixed or variable location), and 4. electricity 92 WM 037-2 EC A paper recommended and approved by the IEEE Energy Development and Power Generation Committee of the IEEE Power Engineering Society for presentation at the IEEE/PES 1992 Winter Meeting, New York, New York, January 26 - 30, 1992. Manuscript submitted April 23, 1991; made available for printing January 9, 1992. K. Ashenayi Member, IEEE Assistant Professor Dept. of Electrical Engineering The University of Tulsa Tulsa, OK USA 74104 Basically, there are two options to supply energy to a variety of loads by utilizing different resources in tandem. ll the resources can be converted to one versatile form (typically electrical) for storage and supply to the users. While this may be convenient, it is not always economical. The alternate approach is to match the xsources, devices, and the needs and achieve integration of benefits at the user end. In most cases, the latter approach results in an econoinically viable option [12]. IRES Design The ultimate objective of any design procedure employed is to obtain the sizes and ratings of the various energy conversion and energy storage devices needed to supply energy to the different loads. Moreover, energy supplies are typically required at a pre- selected reliability level and at minimum cost. Since IRES s very capital intensive and inputs are typically free or inexpensive, minimizing the capital cost will essentially result in a system with minimum annual cost. Some of the resources (wind and insolation) are highly stochastic and site-specific. Others (biomass and hydro) are more predictable, though they have seasonal variations and are also site- specific. Among the loads, some will be more variable than others and some of them can be easily predicted. The design procedure should consider all these factors and include them without unduly expanding he amount of calculations equired. Previous works on IRES design have proposed chronological simulation [13], linear programming[l4,15], goal programming [16], and a probabilistic approach involving the loss of power supply probability (LPSP) as a measure of the quality of the power supply [17]. Chronological simulation requires extensive data on the resources and loads which may not always be available. Linear programming and goal programming approaches are deterministic and they employ seasonal or annual average values in the analyses. In addition, they do not consider the quality (reliability) of the power supply in the design methodologies. Conventional probabilistic approaches require a considerable amount of computations which may not be justifiable. This paper presents a knowledge-based approach to the design of an IRES. t is a modification of the approach presented in Reference 17 in that a knowledge-base and a search algorithm are used in the last stages of the design program instead of a series of long and time-consuming computations. The stored data that go with the knowledge-base have been developed by employing the results documented in a series of two IEEE papers [18,19] on the calculation of the loss of power supply probability of stand-alone wind-electric and photovoltaic systems. n example is presented to illustrate the proposed design approach. OVERALL DESIGN APPROACH The year is divided into as many number of time-sections (seasons) as needed such that the resources and the loads have reasonably similar daily variations or are constant during each period of time. The design procedure is applied for each time-section to find the ratings of energy converters and/or quantities of resources needed and the sizes of energy storage systems to satisfy the energy needs at the desired reliability level subject to resource availabilities 0885-8969/92$03.000 992 EEE  649 and at Mum apital Cost. The final design (for the entire year) is based on these seasonal designs and the prioritization of the various SXWXN- If the final design values are selected based on the worst possible combination of resource availabilities and loads, then a considerable amount of excess energy will be available d&g most of the rest of the time. Unless this excess energy can be productively utilized, the overall design will fail to be cost effective. The information, knowledge, and data base used in the development of the design procedure are discussed next. KNOWLEDGE BASE Energy systems usually consist of several interconnected components performing together to satisfy a set of energy needs. n IRES may consist of solar-thermal collectors, water turbines and pumps, wind-mechanical conversion systems (WMCS), wind- electric conversion systems (WECS), photovoltaic mays (PV), and different types of energy storage and reconversion systems and end- use devices in one or more of many possible combinations to satisfy a variety of energy needs. Different energy resources are variable to different degrees and some of them are complementary over the annual cycle. Loads also vary to different extents and the quality of the energy supply can be characterized by the probability of loss of power supply to the loads. Energy conversion devices are derived from different technologies and are matched to specific situations based on the type of energy supply needed and the end-use scenarios. A knowledge-based design approach can consider all these factors by proper rules of assignment for resources-needs combinations [20,21]. Any energy resource can be used to satisfy all the energy needs by employing a string of energy conversion and interface devices. However, some resource-need combinations are more logical than others because of cost and efficiency considerations. This point has been discussed in detail [22]. The design approach presented in this paper orders, prioritizes, matches, and finally finds the ratings of the various energy conversion devices and sizes of different energy storage components required. Wind Reeimes nd WECS Itis generally accepted that wind regimes can be modeled ushe a Weibull distribution exmessed in terms of a scale Parameter and shape parameter [23]. dese wo parameters can & obtained from the mean and standard deviation of the sample set. For developing the knowledge base, mean wind speeds are quantified into three distinct values: low (4m/s), medium (6m/s) and high (8m/s). With each of these mean wind speeds, t m possible values of standard deviation are considered. Thus nine different wind regimes are employed to characterize the wind resource. Table I lists the nine sets of values used. The choice of these values is based on typical wind regimes that exist around the world [24]. The power output of a WECS depends on the incident wind speed, WECS power rating, and the cut-in, rated, and furling wind speeds. Based on a survey of the specifications of most of the available small(<100 kW) WECS, these wind speeds are taken to be 3.5 m/s 10 m/s, and 22 m/s respectively in this work. Insolation Regimes and PV Although several complex models are available [25], insolation regimes are represented in this work in terms of a beta- distributed random variable 'cloud cover' [26]. The two parameters of the beta distribution can be derived from the mean and standard deviation of the factor by which insolation is reduced as compared o the maximum possible value during the study period. Based on observed insolation data 1271, nine pairs of these parameters are employed to characterize this resource. Table II lists these values. Photovoltaic arrays consist of individual cells fabricated using one of several possible technologies: single-crystal silicon, polycrystalline silicon, amorphous silicon, thin-film devices, etc. The conversion efficiency of a PV module depends on the particular technology employed, insolation, and the module temperature. In this work, a simple model in terms of an average efficiency is used. Also a value of (ln) is assumed for the fraction-of-daytime. Since the objective is to 'design' the system in the presence of a multitude of uncertainties and variabilities, it is believed that a simple model would be adequate for considering the performance of PV systems. Data Base The data base that is an integral part of the knowledge base consists of sets of data relating the amount of energy storage required to the resulting LPSP for various combinations of loads and energy converter ratings under different input (wind and/or insolation) regimes. The energy converter could be PV or WECS or PV and WECS. Non-tracking flat-plate PV arrays rated at 2 to 22 kW in steps of 2 kW are assumed. The rating of the WECS is also varied from 2 to 22 kW in steps of 2 kW. The electrical load is assumed to be unifonnly distributed with its maximum value Lmax ranging from 2 to 12 kW in steps of 2 kW, with et at 10% of Lax he LPSP values considered are allowed to range from 0.02 to 0.18 in increments of 0.02. For the sake of uniformity, a study period of 1 hour is assumed for the data in storage. If all the hours during the study period are similar in the statistical sense , then the values corresponding to any other study period can be obtained by multiplying the hourly values in storage by the number of hours in the study period. For each combination of energy converter(s) rating(s), load, wind regime and/or insolation regime, assuming a study period of one hour, the following quantities are calculated (see References 18 and 19) and stored: (i) ii) E,, the amount of energy input to storage, kwh E,, the amount of energy in kwh required to be drawn from energy storage to achieve an LPSP of zero, and i) set of LPSP and corresponding SSMIN values where SSMTN=E2p-E1 TABLE I WIND REGIME WEIBULL PARAMETERS FOR THE DESIGN EXAMPLE Pwmls a, m/s a m/s P 4 2.00 4.52 2.10 4 2.21 4.5 1 1.88 4 2.78 4.42 1.46 6 6 6 2.50 2.77 3.47 6.76 2.57 6.77 2.30 6.74 1.79 8 2.93 8.96 2.97 8 3.24 9.00 2.66 8 4.07 9.03 2.06 TABLE II CLOUD COVER REGIME BETA PARAMETERS FOR THE DESIGN EXAMPLE Ps 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0s 0.0900 0.1600 0.2200 0.2610 0.288 0.2610 0.2200 0.1600 0.0900 a 1.0111 1.0500 1.0016 1.0093 1.0070 1.5139 2.3372 4.2000 9.1000 P. 9.1000 4.2000 2.3372 1.5139 1.0070 1.0093 1.001 6 1.0500 1.0111  650 in which E2 <E2 is the energy required to be drawn from storage and suppliecfto the load to achieve the assumed value of LPSP. All this information is systematically arranged and stored in data files for easy access. Appendix A shows typical data files for SSMLN versus LPSP curves. With 9 different wind regimes, 9 different insolation regimes, 6 different load levels, 11 different WECS ratings and 11 different PV ratings, a total of 58 104 data sets are calculated and kept in storage as a part of the knowledge base. Enernv Storage Calculatiom As mentioned in the previous section, the values of El, E2, and LPSP versus SSMIN data in storage are all based on a study period of one hour. These values can indeed be multiplied by the number of hours in other study periods under consideration as long as they have similar characteristics. The actual amount of energy storage required for a given study period is calculated using these values as discussed next. During any one time-section (season), the diurnal variations of wind and insolation resources can be assumed to be repetitive in the statistical sense. In addition there will be long-term variations as described by the distribution (or density) function. A close examination of the mechanism of energy storage and withdrawal leads one to conclude that the amount of energy storage required can be assumed to consist of two components: 1. the storage required on a daily (24 hours) basis, and 2. long-term storage to meet the specified LPSP requirements when the resource cannot supply the demand during the study period. If the energy converters (WECS and/or PV) are oversized for the study period under question, then El will be greater than E2 and the amount of energy storage required is simply equal to 24E2. Clearly, under these circumstances, some of the energy generated will be dumped and not utilized. If El is equal to E2, then the amount of energy storage required is equal to El or E2 times 24. In both cases, no additional long-term storage is needed. If E1 is less than E2, then the amount of energy storage required is equal to the sum of 24E1 and the long-term component (SSMINXT) based on the required LPSP. The amount of energy storage as calculated above is multiplied by a factor of 2 to account for the fact that energy storage systems should not be depleted below a certain level (assumed to be 50 ) to preserve their lifetime and reliability attributes. Resource and Need Prioritization Recognizing the fact that under the present economic conditions IRES will be most appropriate to energize remote areas, the energy needs and the resources are prioritized as follows: a. medium-grade heat b. low-grade heat c. mechanical shaft power (fixed or variable location) d. electricity a. biomass (biogas) b. solar-thermal energy C. hydro power d. wind (WECS,WCS) and/or insolation (PV) Needs: Resources: Biogas obtained by anaerobic digestion of biomass is the cheapest energy resource at the present time. It can be burned directly to obtain medium-grade and/or low-grade thermal energy, and can be used in an internal combustion engine to obtain mechanical shaft power and/or electricity by coupling a generator. For these reasons, biogas is given the highest priority. Simple solar collectors can be used to obtain low-grade thermal energy fairly economically. Hydro power can be used via turbines to obtain mechanical shaft power and/or electricity. Wind-mechanical converters also can be used to obtain rotating shaft power. Finally, though relatively expensive, wind and insolation can be converted to electrical form using WECS and PV systems respectively. Based on this brief discussion, the logic behind the ordering of the needs and the resources as given above is evident. The design procedure considered in the next section discusses the fall-back positions for cases where resources are in short supply. DESIGN PROCEDURE The knowledge-based design approach that has been developed is presented in flowchart form in Figure 1.  This computer program will be identified by the acronym IRES-KB. Though this procedure considers a variety of energy resources and loads, the absence of any of these can be easily handled by setting the appropriate quantities equal to zero. The first step in the design procedure is to divide the year into NY time-sections (or seasons) where each season is characterized by a set of resource availabilities and load requirements. As mentioned earlier, the design procedure is applied to each of these NY easons to result in as many seasonal designs. The final design will be selected from these seasonal designs. The next step is to find the maximum area cmax of solar- thermal collector needed to satisfy low-grade thermal energy requirements. This is followed by a determination of the maximum rating wmax of wind-mechanical conversion system (WMCS) needed to power l the rotating shaft loads at fixed locations. These two values are then divided into smaller areas/ratings as dictated by their market availability. The design process starts with a selected set of these two values and goes through a series of steps, culminating in an optimum combination of resource consumption, energy converter ratings, and energy storage device sizes that minimizes the total installed cost. Medium-grade heat requirements are always satisfied by biogas, if available. Any portion that is not satisfied is added to the electrical load for that season with suitable conversion factors. Low-grade heat requirements are satisfied by a combination of biogas and one of the solar collector segments chosen. The portion of low-grade heat that is not satisfied is added to the electrical load for that season. In the next step, mechanical shaft power requirements at fixed locations are satisfied by a combination of biogas-fueled engines and WMCS rated at a fraction of the maximum rating wmax calculated earlier. Any portion of this of load that is not satisfied by this combination is added to the electrical load. Mechanical shaft power at variable locations is satisfied by biogas, if still available. The part that is not fulfiied is once again added to the electrical load as appropriate At this stage in the design process, the only load left to be satisfied is electrical, which is an aggregate of all the additions discussed above and the loads that were electrical to start with. The electrical loads are satisfied by biogas-fueled-engine driven generators if there is still any biogas left. Any leftover loads are supplied by water-turbine-driven generators if available. Finally, the remaining portion of the electrical loads is satisfied by a combination of WECS and/or PV with energy storage. The design procedure has reached the point at which it is required to find the ratings of WECS and/or PV and the size of energy storage required to satisfy a certain electrical load at a predetermined reliability level (as quantified by LPSP) and at minimum capital cost. To accomplish this, the data base in storage is searched to find all combinations of WECS and/or PV and energy storage that will meet the LPSP requirement. The capital costs of these combinations are calculated and the one that results in miniinum cost is chosen. This design process is repeated for each time-section (season) to obtain a set (NY n number) of designs. The design values for each season will consist of solar collector ma, WMCS rating, WECS rating, PV array rating, ratings of water turbines, biogas-fueled engines and engine-generators, ue of energy storage, amount of biogas required, and the amount of water storage. The entire design procedure described thus far is repeated for all the different and reasonable combinations of solar collector areas and WMCS ratings. The final (applicable for the entire year) design values are selected from these seasonal designs. DESIGN EXAMPLE A remote agricultural village with no electrical grid connection and a population of 3 11 is chosen as an example for the application of IRES-KB presented in this paper. The number of cattle is 367. The mount of animal and agricultural wastes available is seasonal as expected.  65 1 enera need are M for MGH L forLGH R forMRS F for FRS E for EL flat plate sc areas : = 0,cmax WCS atings w = 0,wmax U do c = o,cmax - o w = o,wmax +- o s = 1,NY - atisfy M(s ) by BI(s ) I II RM(s ) = 0 BI(s ) = remaining E(s ) = E(&) + RM(s ) BI(s ) = 0 satisfy L by I E@#) = E(&) RL(s ) BI(s ) = 0 I(s ) = remaining satisfy F by . RF(s ) = 0 E(s ) = E( ) + RF(s ) r T I= I(s ) = remaining E(s ) = E@#) RR(s ) BI(s ) = 0 t l atisfy use data-base to \ fiidr;tings optimum rating minimum cost xit with optimum design Figure 1. IRES-KB Plowchart.
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