The Synthesis Problem of Decentralized Energy Systems Is Strongly NP-Hard

The Synthesis Problem of Decentralized Energy Systems is strongly NP-hard

Sebastian Goderbauera,b,∗, Martin Comisa, Felix J. L. Willamowskib

aLehrstuhl II f¨urMathematik, RWTH Aachen University, Aachen, Germany bLehrstuhl f¨ur Operations Research, RWTH Aachen University, Aachen, Germany

Preprint: repORt 2018-043, Operations Research, RWTH Aachen University

Abstract We analyze the computational complexity of the synthesis problem of decentralized energy systems. This synthesis problem consists of combining various types of energy conversion units and determining their sizing as well as operations in order to meet time-varying energy demands while maximizing an objective function, e.g., the net present value. In this paper, we prove that the synthesis problem of decentralized energy systems is strongly NP-hard. Fur- thermore, we prove a strong inapproximability result. This paper provides the first complexity findings in the long scientific history of the synthesis problem of decentralized energy systems. Keywords: Computational complexity, NP-hardness, Inapproximability, Structural optimization, Optimal design and operation, Superstructure

1. Introduction

Decentralized energy is converted in the vicinity of its consumers rather than at a larger centralized plant and sent through a national grid or pipelines. This local generation allows reductions in transmission losses and carbon emissions as

5 well as an increase in supply security. In energy systems, diﬀerent forms of con- sumable energy are provided by various supplier technologies. The application of decentralized energy systems (DES) encompasses, e.g., chemical parks [1], urban districts [2, 3], hospitals and research complexes [4, 5]. DES are highly integrated systems due to a multitude of technical units providing diﬀerent en-

10 ergy forms, energy distribution infrastructure, and connection to the energy market. Energy cost usually match company’s proﬁts in magnitude [6]. Thus, optimally designed decentralized energy systems not only save primary energy, but also considerably increase the system’s proﬁtability. An example for a DES is shown in Figure 1.

∗Corresponding author Email address: [email protected] (Sebastian Goderbauer)

Preprint submitted to ... December 12, 2018 Boiler CHP engine Legend: Electricity Heat Natural gas Cooling hook-up

Absorption chiller Turbo chiller Power supply

. heat . cool Electricity . el Heat E Cooling E E demands demands demands

Figure 1: Example of a decentralized energy system [8].

15 The goal of the synthesis problem of DES is the identiﬁcation of an (econom- ically) optimal energy system, i.e., a best possible selection of energy conversion technologies with optimal unit dimensions, while simultaneously considering its optimal operation. Thus, three types of decisions can be identiﬁed [7]: 1. Synthesis: How many energy conversion units of what technology should

20 be built? 2. Design: How should the technical speciﬁcations, e.g., capacities, of these units be chosen? 3. Operation: How should units be operated in each load case? These three decision levels inﬂuence each other and thus, global optimal

25 solutions can only be obtained if all three levels are simultaneously considered in an integrated optimization approach. Regarding the complexity of the synthesis problem of decentralized energy systems, the comprehensive literature shows a consensus that the problem is, in some sense, diﬃcult to solve: It is “a complex and diﬃcult problem” [5], “a

30 complex problem” [3, 9], “an inherently diﬃcult problem” [10], “a complex and hard task” [11], “not a trivial task” [12], and a problem where it is “diﬃcult to obtain the optimal solution” [13]. However, these complexity claims are not formally proven. Instead, argumentations are based on the number of decisions to be taken, combinatorial options, or amount of input data which is generally

35 not suﬃcient. Above all, the simple fact that an (optimization) problem can be formulated as a, e.g., mixed-integer (non)linear program (MI(N)LP), does not allow any conclusion about the problem’s computational complexity. Formulat- ing and solving an optimization problem via MI(N)LP techniques is only one solution approach. However, to characterize a problem’s computational com-

40 plexity, we have to answer the question whether there is any solution approach that solves the problem eﬃciently. In 1998, Iyer and Grossmann emphasized that synthesis problems of energy systems “require specialized algorithms for their eﬃcient solution” [14]. Elsido

2 et al. underlined in 2017 that “devising an eﬃcient algorithm for tackling the

45 [...] design optimization problem of complex networks of CHP units is still an open challenge” [15]. We answer this open challenge within our paper by proving the folklore result about the problem’s diﬃculty in rigorous terms: There is no polynomial time algorithm, i.e., no eﬃcient one, for solving the synthesis problem of decentralized energy systems unless P = NP. Moreover, we prove that

50 no polynomial time algorithm exists which guarantees a constant approximation factor for the considered synthesis problem unless P = NP. Complexity analyses are an active area of inquiry in the ﬁeld of process system engineering. In a pioneering work [16], Ahmed and Sahinidis prove that the process planning problem is weakly NP-hard. Shortly after, Furman and

55 Sahinidis initiate the extensive complexity research on the problem of heat exchanger network synthesis by showing NP-hardness in the strong sense [17]. In a follow-up work [18] the same authors present approximation algorithms, i.e., heuristics with guaranteed performance. Recently, Letsios et al. [19] provide an alternative NP-hardness proof and additional approximation algorithms. In

60 the wake of the upcoming interest in complexity analyses, also other problems received attention. The problem of sensor placement in water distribution networks is proven to be strongly NP-hard [20]. The pooling problem is proven to be strongly NP-hard by Alfaki and Haugland [21]. Later, approximation algorithms and complexity results for special cases of the pooling problem are

65 presented in [22] and [23]. A rigorous analysis of the computational complexity of the synthesis problem of decentralized energy systems is missing to date. The following sections close that gap for this highly relevant and otherwise exten- sively studied synthesis problem. For a detailed treatment of complexity theory including classes P and NP

70 as well as the most-widely believed theoretical complexity assumption P 6= NP, we refer to [24] and [25, Chapter 15]. In addition, we strongly recommend the three-page introduction into computational complexity theory in the paper of Furman and Sahinidis [17, Section 2]. Among other things, the authors intro- duce the concept of polynomial reductions and provide an illustrative example

75 why a problem’s computational complexity cannot be derived from a (MI(N)LP) formulation in general. The paper is structured as follows: In Section 2, we formalize the synthesis problem of DES and present an exemplary setting. In Section 3, we prove three complexity results: NP-hardness in Section 3.1, NP-hardness in the strong sense

80 in Section 3.2, and inapproximability in Section 3.3. The paper closes with a conclusion in Section 4.

2. Synthesis Problem of Decentralized Energy Systems

We formalize the synthesis problem of DES by describing given parameters, decisions to be taken, constraints to be fulﬁlled, and the objective to be op-

85 timized in Section 2.1. It is assumed that all parameters and data belong to the set of rational numbers. For a summarizing overview, we formulate the

3 optimization problem as a mathematical program. The reason why we state a model is due to the fact that the formalism of mathematical programs ensures an unambiguous problem deﬁnition. In addition to the formal problem deﬁni-

90 tion and mathematical model in Section 2.1, we present a practical setting in Section 2.2.

2.1. Problem deﬁnition We consider the widely used concept of superstructure-based synthesis [26]. For the synthesis problem of DES, a superstructure includes all available con-

95 version units that can potentially be part of the energy system and can be operated to meet the energy demands [27, 28, 29]. The energy demands of the DES vary over time. In order to take this into account, we consider a set of discrete load cases with individual durations and demands speciﬁed for each type of energy. These load cases can be derived from large (real-world) times series

100 via selection of data points or via aggregation to typical periods. We assume a quasi-stationary system behavior, i.e., output energy is immediately available and units have no start-up time. Note, that the assumptions concerning system behavior ensure the independence of load cases, which is a simpliﬁcation of re- ality. Coupling load cases through, e.g., storages or ramping constraints, leads

105 to a more general setting to which all our complexity results can be directly transferred.

2.1.1. Parameters A problem instance consists of a superstructure S, a set of load cases L, and a set of energy forms F . For a subset of the energy forms F mkt ⊆ F market

110 access exists, i.e., they can be traded on the market. The selling and purchase price of energy form f ∈ F mkt is given by pf,sell ≥ 0 and pf,buy ≥ 0, respectively. For each load case ` ∈ L its duration is denoted by ∆` ≥ 0. Parameter ˙ f E` ≥ 0 specifies the demand of energy form f ∈ F in load case ` ∈ L. in For each conversion unit s ∈ S its forms of input energy Fs ⊆ F and output out 115 energy Fs ⊆ F are known. In order to model part-load performance of unit main out s ∈ S, a primary form of output energy fs ∈ Fs is specified. Note, a unique out out main form of output energy, i.e., |Fs | = 1, implies Fs = {fs }. For each unit ˙ N,set s ∈ S a set Vs ⊆ Q≥0 of available capacities, i.e., maximum (nominal) output main energy of form fs , is given. For example, this set can be given in the form of a ˙ N,set ˙ N,min ˙ N,max ˙ N,max ˙ N,min 120 discrete set or an interval Vs = [Vs , Vs ] with Vs > Vs ≥ 0. The investment cost of unit s ∈ S depends on its capacity and is determined ˙ N,set by a given function Is : Vs → Q≥0. Annual maintenance costs of s ∈ S are assumed to be a fixed fraction ms ∈ [0, 1] of the investment cost. To determine the net present value, being the objective function of the problem, the annual

125 present value factor is given as parameter APVF ≥ 0. main The set of possible operation outputs of energy form fs of unit s ∈ S with ˙ N ˙ N,set ˙ set ˙ N ˙ N nominal output Vs ∈ Vs is given as Vs (Vs ) ⊆ [0, Vs ], e.g., this set can be ˙ set ˙ N min ˙ N ˙ N speciﬁed as a discrete set or an interval of the form Vs (Vs ) = [αs · Vs , Vs ] min ˙ set ˙ N with minimum part-load factor αs ∈ [0, 1]. In the following, Vs (Vs ) is

4 ˙ set main 130 regularly abbreviated with Vs . Based on the output of main energy form fs , the required input energy and other output energy is determined by speciﬁed ˙ f ˙ set ˙ N,set in ˙ f ˙ set ˙ N,set functions Us : Vs × Vs → Q≥0 for f ∈ Fs and Vs : Vs × Vs → Q≥0 out main for f ∈ Fs \{fs }, respectively. All functions have to be polynomial time computable, such as discrete functions, polynomial ﬁtted curves, or piecewise

135 linear functions. A summary of all parameters can be found in Table 1.

2.1.2. Decisions We previously identiﬁed three types of decisions: synthesis, design, and operation decisions. To formalize these decisions, we employ the notation of mathematical programming.

140 For every unit s ∈ S of the superstructure, a binary decision variable ys ∈ {0, 1} denotes whether s is set up and thus part of the DES (ys = 1) or ˙ N ˙ N,set not (ys = 0). A decision variable Vs ∈ Vs signiﬁes the (nominal) capacity of a set up unit s ∈ S. A binary decision variable δs` ∈ {0, 1} denotes the on/oﬀ-status (δs` = 1

Load cases

∆` ≥ 0 duration of load case ` ∈ L ˙ f E` ≥ 0 demand of energy form f ∈ F in load case ` ∈ L Conversion units: sizing and cost set of possible nominal output of energy f main V˙ N,set ⊆ s s Q≥0 of unit s ∈ S ˙ N,set Is : Vs → Q≥0 investment cost function of unit s ∈ S ms ∈ [0, 1] maintenance cost factor of unit s ∈ S

Conversion units: part-load performance main ˙ set ˙ N ˙ N set of possible output of energy form fs Vs (Vs ) ⊆ [0, Vs ] ˙ N ˙ N,set of unit s ∈ S with nominal output Vs ∈ Vs function of output energy f ∈ F out \{f main} V˙ f : V˙ set × V˙ N,set → s s s s s Q≥0 of unit s ∈ S ˙ f ˙ set ˙ N,set in Us : Vs × Vs → Q≥0 function of input energy f ∈ Fs of unit s ∈ S Energy market pf,sell ≥ 0 market selling price of energy form f ∈ F mkt pf,buy ≥ 0 market purchase price of energy form f ∈ F mkt

Net present value APVF ≥ 0 annual present value factor

Table 1: Instance data for the synthesis problem of DES.

5 ˙ ˙ set ˙ N 145 means on) and a variable Vs` ∈ Vs (Vs ) represents the output of energy form main ˙ f fs of unit s ∈ S in load case ` ∈ L. Note, by means of the functions Us and ˙ f ˙ Vs (cf. Section 2.1.1), the value of variable Vs` determines the required input in out main energy for all f ∈ Fs and further output energy for all f ∈ Fs \{fs } of unit s ∈ S, respectively. ˙ f,buy ˙ f,sell 150 Finally, continuous decision variables U` ≥ 0 and V` ≥ 0 denote the energy of form f ∈ F mkt bought from and sold on the market in load case ` ∈ L, respectively.

2.1.3. Constraints Logical, technical, environmental, and economical factors imply a set of con-

155 straints that have to be fulﬁlled by DES. ˙ N For each set up unit s ∈ S, its capacity Vs has to be chosen from the ˙ N ˙ N,set set of available capacities, i.e., Vs ∈ Vs (cf. Constraints (2)). Only set up conversion units can be operated in the load cases (cf. Constraints (3)). The output of energy form f main of an operating unit s ∈ S in load case ` ∈ L, ˙ ˙ set ˙ N 160 denoted by Vs`, has to be chosen from the set Vs (Vs ) (cf. Constraints (4)). If s ∈ S is not operated in load case ` ∈ L, it must not generate output and does not require input energy (cf. Constraints (5) and factor δs` in (6)-(7)). For each load case ` ∈ L and each energy form f ∈ F the energy balance equation has to be met, i.e., output minus required input energy of operating ˙ f 165 units has to equal the energy demand E` (cf. Constraints (6)). For energy forms f ∈ F mkt with market access, surplus energy can be sold and additional energy can be bought to equalize the energy balance (cf. Constraints (7)).

2.1.4. Objective The net present value APVF · Rcf − Itot is maximized, where Itot denotes cf cf 170 the total investments and R denotes the net cash ﬂow. The net cash ﬂow R ˙ f,sell mkt is determined as the annual revenue from sold energy V` ≥ 0 for f ∈ F ˙ f,buy mkt minus the cost for energy U` ≥ 0 for f ∈ F bought on the market as ˙ N well as maintenance cost ms · Is(Vs ) for all set up units s ∈ S.

2.1.5. Model

175 A mathematical model for the synthesis problem of DES is given by (1)– (10). The model adopts the previously introduced notation of the parameters and variables. Note, in Objective (1) the negated net present value is minimized which is trivially equivalent to the maximization of the net present value as deﬁned in Section 2.1.4. We present this equivalent version here, since a proof

180 in our complexity analysis is based on an objective that has to be minimized (cf. Section 3.2). In Section 2.1.3, each description of constraints ends with a reference to mathematically formulated Constraints (2)–(7).

6 X X f,sell ˙ f,sell f,buy ˙ f,buy min (−1) · APVF · ∆` · p · V` − p · U` (1) `∈L f∈F mkt ! X ˙ N X ˙ N − ys · ms · Is(Vs ) − ys · Is(Vs ) s∈S s∈S ˙ N ˙ N,set s.t. ys = 1 =⇒ Vs ∈ Vs ∀ s ∈ S (2)

δs` = 1 =⇒ ys = 1 ∀ s ∈ S, ` ∈ L (3) ˙ ˙ set ˙ N δs` = 1 =⇒ Vs` ∈ Vs (Vs ) ∀ s ∈ S, ` ∈ L (4)

δs` = 0 =⇒ V˙s` = 0 ∀ s ∈ S, ` ∈ L (5) X ˙ X ˙ f ˙ ˙ N Vs` + δs` · Vs (Vs`, Vs ) main out s:fs =f s:f∈Fs , main f6=fs ˙ f X ˙ f ˙ ˙ N mkt = E` + δs` · Us (Vs`, Vs ) ∀ ` ∈ L, f ∈ F \ F (6) in s:f∈Fs ˙ f,buy X ˙ X ˙ f ˙ ˙ N U` + Vs` + δs` · Vs (Vs`, Vs ) main out s:fs =f s:f∈Fs , main f6=fs ˙ f X ˙ f ˙ ˙ N ˙ f,sell mkt = E` + δs` · Us (Vs`, Vs ) + V` ∀ ` ∈ L, f ∈ F (7) in s:f∈Fs ˙ N ys ∈ {0, 1}, Vs ≥ 0 ∀ s ∈ S (8)

δs` ∈ {0, 1}, V˙s` ≥ 0 ∀ s ∈ S, ` ∈ L (9) ˙ f,buy ˙ f,sell mkt U` , V` ≥ 0 ∀ ` ∈ L, f ∈ F (10) This generic model for the synthesis problem of DES can be used to formulate both, a MINLP or MILP. To do this, the (indicator) constraints (2) 185 – (5) and the possibly non-linear investment cost functions Is and part-load ˙ f ˙ f performance functions Vs , Us require an appropriate handling. A MINLP formulation, taking into account nonlinearities in the part-load performances and investment cost functions, is given in the work of Goderbauer et al. [8]. Using piecewise-linearized functions, Voll et al. [10] states a MILP formulation.

190 2.2. A practical setting To put the general problem deﬁnition into more concrete terms, we present a practical setting based on the DES example illustrated in Figure 1. This DES setting is inspired by [8, 10] and is used to concretize the constructed problem instances in the proofs of our complexity results in Section 3. Nevertheless, all

195 complexity results presented in this paper hold for the general synthesis problem of DES as deﬁned in Section 2.1.

7 We consider four energy conversion technologies in the supplier subsystem: The superstructure S = B ∪ C ∪ T ∪ A encompasses a set of boilers B, a set of combined heat and power (CHP) engines C, a set of turbo-driven compressor chillers T , and a set of absorption chillers A. Boilers and CHP engines burn gas. Boilers provide heat at high eﬃciency, and CHP engines generate electrical energy (el) and heat simultaneously.

in out Boiler s ∈ B : Fs = {gas},Fs = {heat} in out main CHP engine s ∈ C : Fs = {gas},Fs = {heat, el} with fs = heat

Absorption chillers and turbo chillers provide cooling, requiring heat or electrical energy as input, respectively.

in out Absorption chiller s ∈ A : Fs = {heat},Fs = {cool} in out Turbo chiller s ∈ T : Fs = {el},Fs = {cool}

Market access exists for F mkt = {gas, el} ⊂ F = {gas, heat, cool, el}.

3. Complexity

200 Let SDES be the decision problem whether a feasible solution to the synthesis problem of DES (cf. Section 2.1) with objective value β ∈ Q or less exists. We consider the minimization of the negated net present value as objective as indicated and explained in Section 2.1.5 in conjunction with Section 2.1.4.

3.1. NP-hardness

205 We prove NP-hardness of SDES via a reduction from SUBSET SUM which is known to be NP-complete [30, 24].

SUBSET SUM. Instance: Finite set D, size w(d) ∈ Z≥1 for each d ∈ D, and integer k ≥ 1. 0 210 Question: Is there a subset D ⊆ D such that the sum of the sizes of elements 0 P in D is exactly k, i.e., d∈D0 w(d) = k?

8 Theorem 1. SDES is NP-hard. Proof. We perform a reduction from SUBSET SUM. Let z := (D, w, k) be an instance of SUBSET SUM. In order to avoid the presentation of an overly ab-

215 stract DES setting, we stick to the exemplary setting introduced in Section 2.2, including its conversion technologies and energy forms. In the following, we construct an instance z0(z) of SDES. Figure 2 illustrates the construction. The set of considered energy forms is F := {heat, gas} with market access mkt F := {gas}. The superstructure S := {b1, . . . , b|D|} consists of |D| boil- 220 ers which convert gas into heat. Let π : D → S be a bijection between set D ˙ N,set −1 and superstructure S. For boiler s ∈ S we define Vs := {w(π (s))} and ˙ set ˙ N ˙ N Vs (Vs ) := {Vs }. The instance contains only one load case, L := {1}, with de- ˙ heat ˙ gas mand E1 := k. There is no demand for gas, i.e., E1 := 0. We set investment cost Is := 0 for s ∈ S and annual present value factor APVF := 0. Parameters ˙ gas gas,buy gas,sell 225 ∆1, ms, Us , p , and p can be chosen arbitrarily. By this choice, the objective equals 0 and we consequently set β := 0. Clearly, this instance can be constructed in polynomial time, and its encoding length is polynomially related to that of the input. Now we show that z is a YES-instance of SUBSET SUM if and only if z0(z) is a YES-instance of SDES. Let z = (D, w, k) be a YES-instance of SUBSET SUM with feasible solution D0 ⊆ D. Consider instance z0(z) of SDES. Setting up boilers S0 := {π(d): d ∈ D0} ⊆ S and operating all of them in load case ` = 1 fulfills the energy demand with equality: X ˙ X ˙ N X −1 X ˙ heat Vs1 = Vs = w(π (s)) = w(d) = k = E1 . (11) s∈S0 s∈S0 s∈S0 d∈D0 0 230 Thus, z (z) is a YES-instance of SDES. For the other direction, let z0(z) be a YES-instance of SDES. A feasible solution to z0(z) contains a set of boilers S0 ⊆ S that are operated in load case ˙ heat 0 −1 ` = 1 in order to meet the heat demand E1 = k. We define D := {π (s): 0 P s ∈ S } ⊆ D. Equation (11) holds again, thus we have s∈D0 w(s) = k by 0 235 construction of D which proves that z is a YES-instance of SUBSET SUM with feasible solution D0. Thus, every solution to the SUBSET SUM instance transforms into a solution to the SDES instance, and vice versa. This completes the proof.

Boilers . . .

Natural gas . . . hook-up Heat demand

. heat = . . . E 1 k

Figure 2: Instance of SDES on basis of SUBSET SUM instance in proof of Theorem 1.

9 Corollary 2. There can be no polynomial time algorithm solving the synthesis

240 problem of decentralized energy systems unless P = NP .

Corollary 3. The optimization problem of SDES is NP-hard even for problem settings restricted to one load case and one type of conversion unit generating output form f with f 6∈ F mkt.

Actually, the reduction in the proof of Theorem 1 does not include decisions

245 of the synthesis and design levels. This implies NP-hardness of the operation problem in each load case. In fact, the feasibility problem of the synthesis problem is considered, which implies that the decision problem whether any feasible solution with arbitrary objective value exists is already NP-hard.

Since SUBSET SUM can be solved in pseudo-polynomial time using, e.g.,

250 dynamic programming [31], SUBSET SUM is weakly NP-complete. Thus, the above reduction proves that SDES is (at least) weakly NP-hard for one load case. In the following section, we consider multiple load cases and prove a stronger complexity result for the synthesis problem of DES: NP-hardness in the strong sense.

255 3.2. NP-hardness in the strong sense To prove strong NP-hardness for the synthesis problem of DES we perform a reduction from SET COVER.

SET COVER. 260 Instance: Finite set U = {1, . . . , n}, called universe, a collection A = {A1,...,Am} of subsets of U, i.e., Ai ⊆ U for i = 1, . . . , m, and an integer 1 ≤ k ≤ |A|. Question: Does A contain a cover for U of size k or less, i.e., a subset A0 ⊆ A with |A0| ≤ k such that every element of U belongs to at least one set in A0?

265 The decision problem SET COVER is known to be strongly NP-complete [24]. The corresponding optimization problem MIN SET COVER asks for a cover A0 ⊆ A of smallest size |A0|.

Theorem 4. SDES is strongly NP-hard.

Proof. Let z := (U, A, k) with U = {1, . . . , n}, n ∈ N, A = {A1,...,Am}, and 270 k ≤ |A| be an instance of SET COVER. Again, we stick to the exemplary DES setting given in Section 2.2 to avoid too much abstraction. In the following, we construct an instance z0(z) of SDES. Figure 3 illustrates the construction. The set of considered energy forms is F := {heat, electricity (el), gas} with F mkt := {gas, el}. Let ε > 0 with |U| · ε < 1, e.g., ε := (|U| + 1)−1. The set of load cases is L := U with demands

˙ heat ˙ el 2 ˙ gas E` := `, E` := ` − `ε, E` := 0,

10 and duration ∆` := 1 for ` ∈ L. The superstructure

S := C := {cA : A ∈ A}

consists of |A| CHP engines. Each CHP engine cA ∈ S corresponds to exactly one set A ∈ A and converts F in := {gas} into F out := {heat, el} with f main := cA cA cA ˙ N,set ˙ N heat. We deﬁne Vs := {|U|} for s ∈ S. The investment cost Is(Vs ) := 1 ˙ N of s ∈ S is independent of the selected capacity Vs . Maintenance costs are neglected, i.e., ms := 0 for s ∈ S. For each CHP engine cA ∈ S, A ∈ A, we deﬁne V˙ set := A as well as the part-load performance function cA

U˙ gas(V˙ , V˙ N ) := U˙ gas(V˙ ) := V˙ , cA cA` cA cA cA` cA` and the electricity output

V˙ el (V˙ , V˙ N ) := V˙ el (V˙ ) := V˙ 2 . cA cA` cA cA cA` cA` The remaining parameters are deﬁned as APVF := 1,

pgas,buy := ε, pgas,sell := 0, pel,buy := |A|, and pel,sell := 1.

Clearly, this instance can be constructed in polynomial time and its encoding length is polynomially related to that of the input. Now we show that there is 0 275 a cover A of size β ≤ k for SET COVER instance z if and only if there is a feasible solution with objective value β for SDES instance z0(z). Let A0 be a cover of size β ≤ k ≤ |A| for SET COVER instance z. Setting 0 up CHP engines cA ∈ S corresponding to cover members A ∈ A and choosing their respective capacities as V˙ N = |U| leads to investment cost of β in the cA 280 objective function of SDES. It remains to show that this energy system can satisfy all demands in all load cases with equality, without incurring extra cost. Consider an arbitrary load case `? ∈ L. Since A0 is a cover of U, there exists

CHP engines . . .

Power Natural gas . . . supply hook-up

. . . Heat demands

. . heat el Electricity E = . . . E = demands

Figure 3: SDES instance based on SET COVER instance in proof of Theorem 4.

. E el

. Heat E heat demands ? 0 ? ? ? A ∈ A with ` ∈ A . Operating only CHP engine cA? in load case ` with ? heat ? ˙ ? ˙ VcA? ` = ` satisfies the heat demand E`? = ` . The operated CHP engine el ? ?2 el ?2 ? 285 generates electricity V˙ (` ) = ` . The demand E˙ ? = ` −` ε is fulfilled and cA? ` ˙ el,sell ? ˙ el,sell el,sell ? surplus electricity V`? = ` ε is sold. The revenue V`? p = ` ε equals the operation cost U˙ gas (`?) · pgas,buy = `?ε. Since `? ∈ L was chosen arbitrarily, cA? this holds for every load case ` ∈ L. It follows: We found a feasible solution with objective value β for SDES instance z0(z). 0 290 Now, let a feasible solution for SDES instance z (z) with objective value β ≤ k ≤ |S| be given. We show that this solution consists of exactly β set up CHP engines {c , . . . , c } ⊆ S and that the corresponding set {A ,...,A } ⊆ A Ai1 Aiβ i1 iβ forms a cover for U of cardinality β. Let S` ⊆ S be the set of operated CHP engines in load case ` ∈ L (and thus a subset of all set up ones). We start by showing that the total operating cost in each load case ` ∈ L is non-negative, i.e., no profit can be generated by operating units. The only possible way to gain profit in a load case is by ˙ el ˙ set ˙ ˙ 2 selling surplus electricity. Since Vs : Vs → Q≥0, Vs` 7→ Vs` is (i) a (strictly) superadditive function and (ii) E˙ heat = ` = P V˙ holds, it follows that ` s∈S` s`

(i) X ˙ el ˙ ˙ el X ˙ (ii) ˙ el 2 Vs (Vs`) ≤ Vs Vs` = Vs (`) = ` . (12)

s∈S` s∈S`

˙ el,sell 2 ˙ el Consequently, V` ≤ ` − E` = ε` electrical energy can be sold with revenue el,sell 295 p = 1 per unit. Since operating all CHP engines in S` leads to gas cost of pgas,buy · ` = ε`, the total operating costs for all ` ∈ L are non-negative. We now show that |S`| = 1 holds for all ` ∈ L. To that end, assume the contrapositive, i.e., that there exists `? ∈ L in which (at least) two set up CHP P heat ? engines are operated (|S ? | ≥ 2). It holds that V˙ ? = E˙ ? = ` . In ` s∈S`? s` ` ˙ set ? combination with |S`? | ≥ 2 and Vs ⊂ Z≥1 for s ∈ S this implies that ` ≥ 2 and with Lemma 9 in the Appendix we can strengthen our previous estimation in Equation (12) to

Lem. 9 X ˙ el ˙ ˙ el X ˙ ˙ el ? ?2 Vs (Vs`? ) ≤ Vs Vs`? − 2 = Vs (` ) − 2 = ` − 2.

s∈S`? s∈S`?

? P el By construction, |L|ε < 1 and thus ` ε < 1, which implies V˙ (V˙ ? ) ≤ s∈S`? s s` ?2 ?2 ? ˙ el ˙ el,buy ` − 2 < ` − ` ε − 1 = E`? − 1. Consequently, U`? > 1 electricity has to be el,buy ˙ el,buy bought, implying cost p · U`∗ > |A| = |S| ≥ k in the objective of SDES. 300 As we have previously shown that no profit can be generated that could reduce this cost, this yields a contradiction to the fact that the given solution for the SDES instance z0(z) has an objective value β ≤ k. It follows that for each load case ` ∈ L we have |S`| = 1, say S` = {s`}. ˙ ˙ heat CHP engine s` must fulfill the heat demand, i.e., Vs`` = ` = E` , and ` ∈ set el 2 el 305 V˙ . Consequently, s generates electricity V˙ (`) = ` . The demand E˙ = s` ` s` ` 2 ˙ el,sell ` − `ε is fulfilled and surplus electricity V` = `ε is sold. The revenue

12 V˙ el,sellpel,sell = `ε equals the operation cost U˙ gas(`) · pgas,buy = `ε and the total ` s` operation cost is 0. Thus, we can conclude that the given solution for SDES instance z0(z) with z0(z) 310 objective value β ≤ |S| must contain exactly β set up CHP engines C := {c , . . . , c } ⊆ S causing investment costs β and operation cost 0. For Ai1 Aiβ 0 all ` = 1,..., |U| CHP engine s = c ∈ Cz (z) satisﬁes ` ∈ V˙ set = A . ` Aj` cA j` j`

Thus, {Aj` : ` ∈ L} ⊆ {Ai1 ,...,Aiβ } is a set cover for U which implies that 0 z0(z) 0 A := {Ai1 ,...,Aiβ } induced by C is a set cover of size β for z (z). 0 315 In summary, a cover A of size β ≤ |A| for SET COVER instance z transforms into a feasible solution with objective value β ≤ |S| for SDES instance 0 z (z), and vice versa. This completes the proof. Corollary 5. There can be no pseudo-polynomial time algorithm solving the synthesis problem of decentralized energy systems unless P = NP.

320 Corollary 6. The optimization problem of SDES is NP-hard even for problem settings restricted to one type of conversion unit with two diﬀerent forms of mkt output energy f1, f2 and f1 ∈ F . Remark, that the SDES instance z0(z) constructed in the proof of Theorem 4 does not obey the law of conservation of energy. Using the argumentation from the proof above, this can be circumvented by the following slight adjustment of the instance z0(z):

2 ` ` `ε [ a E˙ heat := , E˙ el := − , V˙ N,set := {1} , V˙ set := , ` |U| ` |U| |U|2 cA cA |U| a∈A

gas gas,buy ε el,buy 2 el,sell U˙ (V˙c `) := 2 · V˙c `, p := , p := |A| · |U| , p := |U|. cA A A 2 Since 0 < V˙ ≤ 1 holds for all V˙ ∈ V˙ set, c ∈ S, the inequality 2 · V˙ ≥ cA` cA` cA A cA` V˙ + V˙ 2 and therefore U˙ gas(V˙ , V˙ N ) ≥ V˙ + V˙ el (V˙ , V˙ N ) is fulﬁlled. cA` cA` cA cA` cA cA` cA cA` cA

325 3.3. Inapproximability After proving that it is strongly NP-hard to compute an optimal solution for the synthesis problem of DES, the question regarding approximability arises: Is it possible that a polynomial time algorithm exists which guarantees a (constant) approximation factor for the considered synthesis problem? We show

330 that under the assumption that P 6= NP, the answer is: No.

The following corollary is a direct consequence from Theorem 4, i.e., from the fact that the considered synthesis problem is strongly NP-hard (cf. [24]).

Corollary 7. For the synthesis problem of decentralized energy systems no fully

335 polynomial time approximation scheme (FPTAS) exists, i.e., no algorithm exists that solves the problem for any ε > 0 within a factor of (1 + ε) of the optimal value in polynomial time with respect to the input size and 1/ε, unless P = NP.

13 The non-existence of an FPTAS itself does not exclude the existence of an eﬃcient algorithm with, e.g., constant approximation guarantee. We strengthen

340 the inapproximability result with the following theorem. Recall that |L| denotes the number of load cases of an instance for the synthesis problem of DES.

Theorem 8. Let 0 < ε < 1. There can be no ε·ln(|L|)-approximation algorithm for the synthesis problem of decentralized energy systems unless P = NP.

Proof. Moshkovitz [32] and Dinur et al. [33] showed that for 0 < ε < 1 there 345 is no ε · ln(|U|)-approximation algorithm for MIN SET COVER (with universe U) unless P = NP. Since the reduction in the proof of Theorem 4 from SET COVER to SDES is cost preserving, the statement follows directly from the inapproximability result for MIN SET COVER.

4. Conclusion

350 This work rectiﬁes the lack of formal results in the computational complexity analysis of the synthesis problem of decentralized energy systems. We present reductions from known (strongly) NP-hard problems to prove that the considered synthesis problem is diﬃcult to solve from a theoretical point of view as there exists no polynomial time solution algorithm unless P = NP.

355 We specify the considered problem setting and formalize the synthesis problem of DES in form of a mathematical model with an objective, constraints, variables, and required input data. After that, we present three complexity results: As part of the synthesis problem, we prove that (i) already the operation problem of one load case is weakly NP-hard. Furthermore, we strengthen this

360 complexity statement and prove that (ii) the synthesis problem of DES is NP- hard in the strong sense. Since our conducted reduction is cost preserving, it turns out that it is even (iii) NP-hard to approximate an optimal solution with a constant approximation factor. Previous publications proposing solution approaches for synthesis of DES

365 explain the diﬃculty of the problem by means of experience and insuﬃcient arguments. In contrast, this work provides well-founded proofs on the computational complexity. Our results justify the use and development of heuristics and algorithms with potentially exponential runtime to compute feasible solutions or even to solve the synthesis problem of DES optimally or approximately.

370 Appendix

Lemma 9. For a1, . . . , an ∈ N \{0}, n ≥ 2 holds:

n !2 n X X 2 ai − ai ≥ 2. i=1 i=1

14 Proof. By the multinomial theorem, we have

n !2 n n X X 2 Y ki X 2 X ai = ai = ai + 2aiaj. k1! · ... · kn! i=1 k1+...+kn=2 i=1 i=1 1≤i

n !2 n X X 2 X ai − ai = 2aiaj ≥ 2. i=1 i=1 1≤i

Note that the lower bound of 2 is attained for n = 2 and a1 = a2 = 1.

Acknowledgments

The authors would like to thank Bj¨ornBahl for making us familiar with the synthesis problem of decentralized energy systems, as well as Andreas Tillmann,

375 Marco L¨ubbecke, Arie M.C.A. Koster, and the anonymous reviewers for valuable comments on an earlier version of the manuscript.

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